From 7d011b23a0766b93df49a4528be31c748f77aa89 Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Sun, 2 May 2021 16:27:45 +0200 Subject: [PATCH 01/21] Add SubjQA dataset --- datasets/subjqa/subjqa.py | 127 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 datasets/subjqa/subjqa.py diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py new file mode 100644 index 00000000000..c67b90551f9 --- /dev/null +++ b/datasets/subjqa/subjqa.py @@ -0,0 +1,127 @@ +# coding=utf-8 +# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SubjQA is a question answering dataset that focuses on subjective questions and answers. +The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery, +electronics, TripAdvisor (i.e. hotels), and restaurants.""" + + +import csv +import json +import os + +import datasets + + +_CITATION = """\ +@inproceedings{bjerva20subjqa, + title = "SubjQA: A Dataset for Subjectivity and Review Comprehension", + author = "Bjerva, Johannes and + Bhutani, Nikita and + Golahn, Behzad and + Tan, Wang-Chiew and + Augenstein, Isabelle", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing", + month = November, + year = "2020", + publisher = "Association for Computational Linguistics", +} +""" + +_DESCRIPTION = """SubjQA is a question answering dataset that focuses on subjective questions and answers. +The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery, +electronics, TripAdvisor (i.e. hotels), and restaurants.""" + +_HOMEPAGE = "" + +_LICENSE = "" + +_URLs = {"default": "https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip"} + + +class Subjqa(datasets.GeneratorBasedBuilder): + """SubjQA is a question answering dataset that focuses on subjective questions and answers.""" + + VERSION = datasets.Version("1.1.0") + + BUILDER_CONFIGS = [ + datasets.BuilderConfig(name="books", version=VERSION, description="Amazon book reviews"), + datasets.BuilderConfig(name="electronics", version=VERSION, description="Amazon electronics reviews"), + datasets.BuilderConfig(name="grocery", version=VERSION, description="Amazon grocery reviews"), + datasets.BuilderConfig(name="movies", version=VERSION, description="Amazon movie reviews"), + datasets.BuilderConfig(name="restaurants", version=VERSION, description="Yelp restaurant reviews"), + datasets.BuilderConfig(name="tripadvisor", version=VERSION, description="TripAdvisor reviews"), + ] + + DEFAULT_CONFIG_NAME = "" # It's not mandatory to have a default configuration. Just use one if it make sense. + + def _info(self): + features = datasets.Features( + { + "item_id": datasets.Value("string"), + "domain": datasets.Value("string"), + "nn_mod": datasets.Value("string"), + "nn_asp": datasets.Value("string"), + "query_mod": datasets.Value("string"), + "query_asp": datasets.Value("string"), + "q_review_id": datasets.Value("string"), + "q_reviews_id": datasets.Value("string"), + "question": datasets.Value("string"), + "question_subj_level": datasets.Value("int64"), + "ques_subj_score": datasets.Value("float"), + "is_ques_subjective": datasets.Value("bool"), + "review_id": datasets.Value("string"), + "review": datasets.Value("string"), + "human_ans_spans": datasets.Value("string"), + "human_ans_indices": datasets.Value("string"), + "answer_subj_level": datasets.Value("int64"), + "ans_subj_score": datasets.Value("float"), + "is_ans_subjective": datasets.Value("bool"), + } + ) + return datasets.DatasetInfo( + description=_DESCRIPTION, + features=features, + supervised_keys=None, + homepage=_HOMEPAGE, + license=_LICENSE, + citation=_CITATION, + ) + + def _split_generators(self, dl_manager): + data_dir = dl_manager.download_and_extract(_URLs["default"]) + return [ + datasets.SplitGenerator( + name=datasets.Split.TRAIN, + gen_kwargs={"filepath": os.path.join(data_dir, + f"SubjQA-master/SubjQA/{self.config.name}/splits/train.csv")}, + ), + datasets.SplitGenerator( + name=datasets.Split.TEST, + gen_kwargs={"filepath": os.path.join(data_dir, + f"SubjQA-master/SubjQA/{self.config.name}/splits/test.csv")}, + ), + datasets.SplitGenerator( + name=datasets.Split.VALIDATION, + gen_kwargs={"filepath": os.path.join(data_dir, + f"SubjQA-master/SubjQA/{self.config.name}/splits/dev.csv")}, + ), + ] + + def _generate_examples(self, filepath): + with open(filepath, encoding="utf-8") as csv_file: + csv_reader = csv.reader(csv_file, delimiter=",") + column_names = next(csv_reader) + for _id, row in enumerate(csv_reader): + yield _id, {column_name:value for column_name, value in zip(column_names, row)} From cf6a90da543a50b9eaa3f1b574ba1e5fab0f5b10 Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Sun, 2 May 2021 16:39:47 +0200 Subject: [PATCH 02/21] Remove unused code --- datasets/subjqa/subjqa.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index c67b90551f9..2ae45131e37 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -18,7 +18,6 @@ import csv -import json import os import datasets @@ -61,11 +60,9 @@ class Subjqa(datasets.GeneratorBasedBuilder): datasets.BuilderConfig(name="grocery", version=VERSION, description="Amazon grocery reviews"), datasets.BuilderConfig(name="movies", version=VERSION, description="Amazon movie reviews"), datasets.BuilderConfig(name="restaurants", version=VERSION, description="Yelp restaurant reviews"), - datasets.BuilderConfig(name="tripadvisor", version=VERSION, description="TripAdvisor reviews"), + datasets.BuilderConfig(name="tripadvisor", version=VERSION, description="TripAdvisor hotel reviews"), ] - DEFAULT_CONFIG_NAME = "" # It's not mandatory to have a default configuration. Just use one if it make sense. - def _info(self): features = datasets.Features( { From f777a2a826a80b997deea9192d2097a22b653f3b Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Sun, 2 May 2021 16:39:54 +0200 Subject: [PATCH 03/21] Add README --- datasets/subjqa/README.md | 234 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 datasets/subjqa/README.md diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md new file mode 100644 index 00000000000..300e70d56be --- /dev/null +++ b/datasets/subjqa/README.md @@ -0,0 +1,234 @@ +--- +annotations_creators: +- expert-generated +language_creators: +- found +languages: +- en +licenses: +- unknown +multilinguality: +- monolingual +size_categories: +- 1K Date: Sun, 2 May 2021 16:46:22 +0200 Subject: [PATCH 04/21] Add dataset infos and dummy data --- datasets/subjqa/dataset_infos.json | 1 + .../subjqa/dummy/books/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes .../dummy/electronics/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes .../subjqa/dummy/grocery/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes .../subjqa/dummy/movies/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes .../dummy/restaurants/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes .../dummy/tripadvisor/1.1.0/dummy_data.zip | Bin 0 -> 60122 bytes 7 files changed, 1 insertion(+) create mode 100644 datasets/subjqa/dataset_infos.json create mode 100644 datasets/subjqa/dummy/books/1.1.0/dummy_data.zip create mode 100644 datasets/subjqa/dummy/electronics/1.1.0/dummy_data.zip create mode 100644 datasets/subjqa/dummy/grocery/1.1.0/dummy_data.zip create mode 100644 datasets/subjqa/dummy/movies/1.1.0/dummy_data.zip create mode 100644 datasets/subjqa/dummy/restaurants/1.1.0/dummy_data.zip create mode 100644 datasets/subjqa/dummy/tripadvisor/1.1.0/dummy_data.zip diff --git a/datasets/subjqa/dataset_infos.json b/datasets/subjqa/dataset_infos.json new file mode 100644 index 00000000000..1e4af3bcdfb --- /dev/null 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CAd>L>skPNS4p z0>cTi!UVA}-dtM(=isZ&_oh7pTR&KNmO?J`O52Bg5MVjoCr;(Y^qFLx6-!A8n4Uz) z&4}j2NaIH|QhHL%;PT8nw}i)O3l!!kO{p&}0L`KU=5hjtN=0Sw3&q(AN6PEoE51V= zD8UMhvqaR^!AG!QH79>hZVsGs%Ah6g!p{?ctSqcBIb;WAqd6IEgyDyS_Idkk0#Vb_ z?{h3f)A#M|i(^FMMolDo66(}=SQ)iJPe`e5FJP|QS-I|7wKX|VU-dOIoe zQZ^v<)v(+$cT3h*pCCy!ump!p#v7kFQ6%{ZyA&wNBt1F1slb0Rsf}2;wRlFh&z5%8 zTD8~f?Yb^uP>xRe9Alkw3)qYmiMm&HtzYpj>C2Xbp>04r`Ko?BtdMUvcX@@$X%@?` zdmnW-yT2djV-aSsiOLaRQ3H9y$3JZBV zO8?xvVi$d8TRR;Hqc9VX%sjt1 zom2J$!spexx7{UQ&XUVF-jed!hYEY8kPuo7t`dBLXD&UPlGsEXgQIgPm&K_EJB+g^ z@_VKtyOihVyW_D9Ps9T0I7s^q+sGap7bLrw1tj1RJ@gIa@AQI}%b>%i-e=T9^|%%h zS@)P|Za<;yP%fYEOEs)emq4?!Et=bk{Gh~$=C-v~xLzUvdFM|cV1?=27Fno*+St70 z`uce%`sXfeo5@M09O;IZ^w{nhs`T}E98@ao2*K(@O zo~qMeg@|}~xg!cx1Eey;^gI=cq8i?#;L>DJbPb5ejICggNbPLrtR%o7P$F%WOhz-_ zixTo7gYoKAjPOk-!e$+(rR$G@wvNuKaR5K+#=d|Ae<)pWEzUft5I{H3N9h{0IoWO` zG8L<^Ue(v)L9%=*XpqO4Z@>Bo%xSA9VOJA#01yma!bwCW> zc^5iwUva76Oi!j#F=7#5h!lLFkU{yuzZnaSaDP0&LRqrN{>Ua^b9sMhX2Y)=cDe-Q z^Drr;m8H7{a0qS|?g4y}X+-KAtf9xY6-6VYQ@qH5(`&rQ?~i9SEJWfEgkL%Sp{&mj z!QDvUh99_z{EPaK)r-c*cib#b5OHm+V0w_&tH~jAsg(@<=)#iCDJ3~_A$-seV#ChS ztveEHNj@4IJZ@8}L;SYA9TJ-3G%Zg`YfRsmw;E_Qt~{9KQ1Io5Ox z6Lj5Kl>_1vu>=*5Clw?#n2D(RbX>*ovKJ;Nn;LO91pKxGPXXG{6VZ_D1BQz`J`!!v z24raG$sFtHYc4*6Y42Iio|kBE^L-BwDzdmwU$1ex;sfhB;P~F;P>cyuF z?t7R7wce2@S&<}H+MS)h<*(;BW`ab40k|DPC=UL%qHtCRv$pFW(sv~!-)w;q%9QOu z6@rRVQ?BajwHOJo4Z)N65;e}ILhZFjsEG7k;VBY!8_+j1&Wl;Sm?itDbQ2VbY;U-+ z@!rd1Ivg9!t5zQpf>qhV`yWv#q(Fk`x`PRr-rmN|QF;Z5@aNFzbQa7`iK`nj*Lzk- zTlNg7Jl85#`3X`-6Yat^R)3Y&=r$Y6bJ{nH9f=UV7S)hdi!Y&k)V7*RNqAR|5Wd)N zK$_zwbA@8T7(C(|p<;VE?#0sFZWQU0s_--hCHU3G1Dha6p^(I_Z8g*r%Wp>$L<-}t zCIEBg%vCX>RV?2icNGwk2#^U*ufzOrfE4CmpPoJ8BCMPx@kcOru(W*o(AdDC}_4*cEDY=ADbq2^#{c2Au3yVc)XWcgw6 zFL&bqrN!^|e`}cY7sCsGX4uf$+T8B9=J|dx&xd$XZ~od7fB$Z<1=z^m*4oO{==Tur zUm!t$2KiC3;dlE4*DrvvKLh-|QsQ^^f9)k!q(AB>B)an8U8rLzlREGk)jG2p&}r#;(d=({6S0h;zs_zd+l%5 zkEq3;{(Wbv7xc^ro|XcEot9ZX3s3sdlD!VlFU$NpSm`{n0_vyl%;?rx*XV;m3 zv}CV^8<$1?_p0+x=>HC%zv}2o`$y1!9=8|h7{3{lFLSD)sLFXAM=#B~NXKbpCN-MV zvM%+fp=Q78_?-8${#~FW=BnCG&*`qSLUUe643Wzs|CNsXKlYrz<_&SBKZ1TA-Y-xP z^pTJn{j?{yPZ47Or63?AdqrM?a*=k*ErbI$r*nG!>GUl63+-6v|M%$v)y~&d|G`W4 zYBl-)VgE|6v+{4>+u8UDbNVCPkE!ee#m<+5`$0nQk$me+2w>E#=Q)`TO*9K7a2IP_oz8%U~{2?R?g(pSYzr zm+ Date: Sun, 2 May 2021 16:46:34 +0200 Subject: [PATCH 05/21] Fix style --- datasets/subjqa/subjqa.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index 2ae45131e37..ed8ea5354c8 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -101,18 +101,21 @@ def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, - gen_kwargs={"filepath": os.path.join(data_dir, - f"SubjQA-master/SubjQA/{self.config.name}/splits/train.csv")}, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/train.csv") + }, ), datasets.SplitGenerator( name=datasets.Split.TEST, - gen_kwargs={"filepath": os.path.join(data_dir, - f"SubjQA-master/SubjQA/{self.config.name}/splits/test.csv")}, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/test.csv") + }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, - gen_kwargs={"filepath": os.path.join(data_dir, - f"SubjQA-master/SubjQA/{self.config.name}/splits/dev.csv")}, + gen_kwargs={ + "filepath": os.path.join(data_dir, f"SubjQA-master/SubjQA/{self.config.name}/splits/dev.csv") + }, ), ] @@ -121,4 +124,4 @@ def _generate_examples(self, filepath): csv_reader = csv.reader(csv_file, delimiter=",") column_names = next(csv_reader) for _id, row in enumerate(csv_reader): - yield _id, {column_name:value for column_name, value in zip(column_names, row)} + yield _id, {column_name: value for column_name, value in zip(column_names, row)} From c47c3f413fb6f7620bf689ca36d1bf83be548536 Mon Sep 17 00:00:00 2001 From: lewtun Date: Mon, 3 May 2021 17:59:33 +0200 Subject: [PATCH 06/21] Update datasets/subjqa/README.md Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> --- datasets/subjqa/README.md | 52 +++++++++++++++++---------------------- 1 file changed, 23 insertions(+), 29 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 300e70d56be..d764a29efa5 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -22,34 +22,28 @@ task_ids: # Dataset Card for subjqa ## Table of Contents -- [Dataset Card for subjqa](#dataset-card-for-subjqa) - - [Table of Contents](#table-of-contents) - - [Dataset Description](#dataset-description) - - [Dataset Summary](#dataset-summary) - - [Data collection](#data-collection) - - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - - [Languages](#languages) - - [Dataset Structure](#dataset-structure) - - [Data Instances](#data-instances) - - [Data Fields](#data-fields) - - [Data Splits](#data-splits) - - [Dataset Creation](#dataset-creation) - - [Curation Rationale](#curation-rationale) - - [Source Data](#source-data) - - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - - [Who are the source language producers?](#who-are-the-source-language-producers) - - [Annotations](#annotations) - - [Annotation process](#annotation-process) - - [Who are the annotators?](#who-are-the-annotators) - - [Personal and Sensitive Information](#personal-and-sensitive-information) - - [Considerations for Using the Data](#considerations-for-using-the-data) - - [Social Impact of Dataset](#social-impact-of-dataset) - - [Discussion of Biases](#discussion-of-biases) - - [Other Known Limitations](#other-known-limitations) - - [Additional Information](#additional-information) - - [Dataset Curators](#dataset-curators) - - [Licensing Information](#licensing-information) - - [Citation Information](#citation-information) +- [Dataset Description](#dataset-description) + - [Dataset Summary](#dataset-summary) + - [Supported Tasks](#supported-tasks) + - [Languages](#languages) +- [Dataset Structure](#dataset-structure) + - [Data Instances](#data-instances) + - [Data Fields](#data-fields) + - [Data Splits Sample Size](#data-splits-sample-size) +- [Dataset Creation](#dataset-creation) + - [Curation Rationale](#curation-rationale) + - [Source Data](#source-data) + - [Annotations](#annotations) + - [Personal and Sensitive Information](#personal-and-sensitive-information) +- [Considerations for Using the Data](#considerations-for-using-the-data) + - [Social Impact of Dataset](#social-impact-of-dataset) + - [Discussion of Biases](#discussion-of-biases) + - [Other Known Limitations](#other-known-limitations) +- [Additional Information](#additional-information) + - [Dataset Curators](#dataset-curators) + - [Licensing Information](#licensing-information) + - [Citation Information](#citation-information) + - [Contributions](#contributions) ## Dataset Description @@ -231,4 +225,4 @@ If you are using the dataset, please cite the following in your work: year = "2020", publisher = "Association for Computational Linguistics", } -``` \ No newline at end of file +``` From 98e4b11f9d6aa23289a7ddf630dc599583f6d81b Mon Sep 17 00:00:00 2001 From: lewtun Date: Mon, 3 May 2021 17:59:40 +0200 Subject: [PATCH 07/21] Update datasets/subjqa/README.md Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> --- datasets/subjqa/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index d764a29efa5..ef05e87e204 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -74,7 +74,7 @@ The SubjQA dataset is constructed based on publicly available review datasets. S The process of constructing SubjQA is discussed in detail in our [paper](https://arxiv.org/abs/2004.14283). In a nutshell, the dataset construction consists of the following steps: 1. First, all _opinions_ expressed in reviews are extracted. In the pipeline, each opinion is modeled as a (_modifier_, _aspect_) pair which is a pair of spans where the former describes the latter. (good, hotel), and (terrible, acting) are a few examples of extracted opinions. -2. Using Matrix Factorization techninques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion. +2. Using Matrix Factorization techniques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion. 3. Annotators are then asked to write a question based on _query_ opinions. For instance given "good keyboard" as the query opinion, they might write "Is this keyboard any good?" 4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys". 5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span. From f7bdc6b53cfe5fc32829c446f35e7774a88e9d17 Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 13:19:03 +0200 Subject: [PATCH 08/21] Refactor SubjQA to produce SQuAD schema --- datasets/subjqa/subjqa.py | 116 +++++++++++++++++++++++++++++++++----- 1 file changed, 102 insertions(+), 14 deletions(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index ed8ea5354c8..14f3e1e6dab 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -17,9 +17,10 @@ electronics, TripAdvisor (i.e. hotels), and restaurants.""" -import csv import os +import pandas as pd + import datasets @@ -66,25 +67,30 @@ class Subjqa(datasets.GeneratorBasedBuilder): def _info(self): features = datasets.Features( { - "item_id": datasets.Value("string"), "domain": datasets.Value("string"), "nn_mod": datasets.Value("string"), "nn_asp": datasets.Value("string"), "query_mod": datasets.Value("string"), "query_asp": datasets.Value("string"), - "q_review_id": datasets.Value("string"), "q_reviews_id": datasets.Value("string"), "question": datasets.Value("string"), "question_subj_level": datasets.Value("int64"), "ques_subj_score": datasets.Value("float"), "is_ques_subjective": datasets.Value("bool"), "review_id": datasets.Value("string"), - "review": datasets.Value("string"), - "human_ans_spans": datasets.Value("string"), - "human_ans_indices": datasets.Value("string"), - "answer_subj_level": datasets.Value("int64"), - "ans_subj_score": datasets.Value("float"), - "is_ans_subjective": datasets.Value("bool"), + "id": datasets.Value("string"), + "title": datasets.Value("string"), + "context": datasets.Value("string"), + "question": datasets.Value("string"), + "answers": datasets.features.Sequence( + { + "text": datasets.Value("string"), + "answer_start": datasets.Value("int32"), + "answer_subj_level": datasets.Value("int64"), + "ans_subj_score": datasets.Value("float"), + "is_ans_subjective": datasets.Value("bool"), + } + ), } ) return datasets.DatasetInfo( @@ -120,8 +126,90 @@ def _split_generators(self, dl_manager): ] def _generate_examples(self, filepath): - with open(filepath, encoding="utf-8") as csv_file: - csv_reader = csv.reader(csv_file, delimiter=",") - column_names = next(csv_reader) - for _id, row in enumerate(csv_reader): - yield _id, {column_name: value for column_name, value in zip(column_names, row)} + df = pd.read_csv(filepath) + squad_format = self._convert_to_squad(df) + for example in squad_format["data"]: + title = example.get("title", "").strip() + for paragraph in example["paragraphs"]: + context = paragraph["context"].strip() + for qa in paragraph["qas"]: + question = qa["question"].strip() + question_meta = {k: v for k, v in qa.items() if k in self.question_meta_columns} + id_ = qa["id"] + answer_starts = [answer["answer_start"] for answer in qa["answers"]] + answers = [answer["text"].strip() for answer in qa["answers"]] + answer_meta = {k: [] for k in self.answer_meta_columns} + for answer in qa["answers"]: + for k, v in answer.items(): + if k in self.answer_meta_columns: + answer_meta[k].append(v) + yield id_, { + **{ + "title": title, + "context": context, + "question": question, + "id": id_, + "answers": { + **{ + "answer_start": answer_starts, + "text": answers, + }, + **answer_meta, + }, + }, + **question_meta, + } + + def _create_paragraphs(self, df): + "A helper function to convert a pandas.DataFrame of (question, context, answer) rows to SQuAD paragraphs." + self.question_meta_columns = [ + "domain", + "nn_mod", + "nn_asp", + "query_mod", + "query_asp", + "q_reviews_id", + "question_subj_level", + "ques_subj_score", + "is_ques_subjective", + "review_id", + ] + self.answer_meta_columns = ["answer_subj_level", "ans_subj_score", "is_ans_subjective"] + id2review = dict(zip(df["review_id"], df["review"])) + pars = [] + for review_id, review in id2review.items(): + qas = [] + review_df = df.query(f"review_id == '{review_id}'") + id2question = dict(zip(review_df["q_review_id"], review_df["question"])) + + for k, v in id2question.items(): + d = df.query(f"q_review_id == '{k}'").to_dict(orient="list") + answer_starts = [eval(a)[0] for a in d["human_ans_indices"]] + answer_meta = {k: v[0] for k, v in d.items() if k in self.answer_meta_columns} + question_meta = {k: v[0] for k, v in d.items() if k in self.question_meta_columns} + # Only fill answerable questions + if pd.unique(d["human_ans_spans"])[0] != "ANSWERNOTFOUND": + answers = [ + {**{"text": text, "answer_start": answer_start}, **answer_meta} + for text, answer_start in zip(d["human_ans_spans"], answer_starts) + if text != "ANSWERNOTFOUND" + ] + else: + answers = [] + qas.append({**{"question": v, "id": k, "answers": answers}, **question_meta}) + # Slice off ANSWERNOTFOUND from context + pars.append({"qas": qas, "context": review[: -len(" ANSWERNOTFOUND")]}) + return pars + + def _convert_to_squad(self, df): + "A helper function to convert a pandas.DataFrame of product-based QA dataset into SQuAD format" + groups = ( + df.groupby("item_id") + .apply(self._create_paragraphs) + .to_frame(name="paragraphs") + .reset_index() + .rename(columns={"item_id": "title"}) + ) + subjqa_data = {} + subjqa_data["data"] = groups.to_dict(orient="records") + return subjqa_data From 20a02f69f36558fb183f931f50d9d7ef9012e4ff Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 13:38:46 +0200 Subject: [PATCH 09/21] Simplify extraction of answer metadata --- datasets/subjqa/subjqa.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index 14f3e1e6dab..07d86e645ad 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -138,11 +138,7 @@ def _generate_examples(self, filepath): id_ = qa["id"] answer_starts = [answer["answer_start"] for answer in qa["answers"]] answers = [answer["text"].strip() for answer in qa["answers"]] - answer_meta = {k: [] for k in self.answer_meta_columns} - for answer in qa["answers"]: - for k, v in answer.items(): - if k in self.answer_meta_columns: - answer_meta[k].append(v) + answer_meta = pd.DataFrame(qa["answers"], columns=self.answer_meta_columns).to_dict("list") yield id_, { **{ "title": title, @@ -210,6 +206,6 @@ def _convert_to_squad(self, df): .reset_index() .rename(columns={"item_id": "title"}) ) - subjqa_data = {} - subjqa_data["data"] = groups.to_dict(orient="records") - return subjqa_data + squad_data = {} + squad_data["data"] = groups.to_dict(orient="records") + return squad_data From e0ae7178c24ff2745acd02bbb27bb1ad6ba1ff7c Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 13:41:30 +0200 Subject: [PATCH 10/21] Remove redundant feature --- datasets/subjqa/subjqa.py | 1 - 1 file changed, 1 deletion(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index 07d86e645ad..4417db1deff 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -73,7 +73,6 @@ def _info(self): "query_mod": datasets.Value("string"), "query_asp": datasets.Value("string"), "q_reviews_id": datasets.Value("string"), - "question": datasets.Value("string"), "question_subj_level": datasets.Value("int64"), "ques_subj_score": datasets.Value("float"), "is_ques_subjective": datasets.Value("bool"), From 87d4d3bac155419f7dc72cd63561198766239d14 Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 13:55:14 +0200 Subject: [PATCH 11/21] Update dataset infos --- datasets/subjqa/dataset_infos.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datasets/subjqa/dataset_infos.json b/datasets/subjqa/dataset_infos.json index 1e4af3bcdfb..8ef13ca17bb 100644 --- a/datasets/subjqa/dataset_infos.json +++ b/datasets/subjqa/dataset_infos.json @@ -1 +1 @@ -{"books": {"description": "SubjQA is a question answering dataset that focuses on subjective questions and answers.\nThe dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,\nelectronics, TripAdvisor (i.e. hotels), and restaurants.", "citation": "@inproceedings{bjerva20subjqa,\n title = \"SubjQA: A Dataset for Subjectivity and Review Comprehension\",\n author = \"Bjerva, Johannes and\n Bhutani, Nikita and\n Golahn, Behzad and\n Tan, Wang-Chiew and\n Augenstein, Isabelle\",\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing\",\n month = November,\n year = \"2020\",\n publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "", "license": "", "features": {"item_id": {"dtype": "string", "id": null, "_type": "Value"}, "domain": {"dtype": "string", "id": null, "_type": "Value"}, "nn_mod": {"dtype": "string", "id": null, "_type": "Value"}, "nn_asp": {"dtype": "string", "id": null, "_type": "Value"}, "query_mod": {"dtype": "string", "id": null, "_type": "Value"}, "query_asp": {"dtype": "string", "id": null, "_type": "Value"}, "q_review_id": 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zz_|?H`j!R-2uSpw#sdN6`7we1Zp1s#DFxmikaFG3LBv~5aQvG(qR2bYBh-Hb=~LqV z0ZpjxpfbJ9=$qbV^kVNoKd^p-KK)zs{>Fv=Gt2)E`tiS6`4auP*T41YKddsZQffut z81c#dC#%)}4*JV#2b4nT<6E|K|64Jz_pE|h{@b1YvU*pT{2eP8p5Gwxe{0_Vigo7M4qz+YbPvee%J zh`rz4;V-jyxoYnKra`{}!hc8lzoL`>r>F28H;#WC{pOYu{o4`5e~tWqsJ#np{>T0| zwTpN5f0@0D4|r#Pl=kob{}Ow*Xnto8SMl4P?0;B8|3{zw8C@-nZwUVQFbcRoqZi~P zARGHlBPHc+aJ+v{U^KlMipRhF>wN=9i3<|n_6HjU0U2*c`CqRgz}sb>(YMP2@oSVw T2t+_cK*#VvKufE?oB4kLIT2Hj From 6a657275d1dde862b8268479322945746d17d5e6 Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 13:56:03 +0200 Subject: [PATCH 13/21] Update README --- datasets/subjqa/README.md | 97 ++++++++++++++++++++++----------------- 1 file changed, 56 insertions(+), 41 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index ef05e87e204..5fd33869ff2 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -22,28 +22,35 @@ task_ids: # Dataset Card for subjqa ## Table of Contents -- [Dataset Description](#dataset-description) - - [Dataset Summary](#dataset-summary) - - [Supported Tasks](#supported-tasks) - - [Languages](#languages) -- [Dataset Structure](#dataset-structure) - - [Data Instances](#data-instances) - - [Data Fields](#data-fields) - - [Data Splits Sample Size](#data-splits-sample-size) -- [Dataset Creation](#dataset-creation) - - [Curation Rationale](#curation-rationale) - - [Source Data](#source-data) - - [Annotations](#annotations) - - [Personal and Sensitive Information](#personal-and-sensitive-information) -- [Considerations for Using the Data](#considerations-for-using-the-data) - - [Social Impact of Dataset](#social-impact-of-dataset) - - [Discussion of Biases](#discussion-of-biases) - - [Other Known Limitations](#other-known-limitations) -- [Additional Information](#additional-information) - - [Dataset Curators](#dataset-curators) - - [Licensing Information](#licensing-information) - - [Citation Information](#citation-information) - - [Contributions](#contributions) +- [Dataset Card for subjqa](#dataset-card-for-subjqa) + - [Table of Contents](#table-of-contents) + - [Dataset Description](#dataset-description) + - [Dataset Summary](#dataset-summary) + - [Data collection](#data-collection) + - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) + - [Languages](#languages) + - [Dataset Structure](#dataset-structure) + - [Data Instances](#data-instances) + - [Data Fields](#data-fields) + - [Data Splits](#data-splits) + - [Dataset Creation](#dataset-creation) + - [Curation Rationale](#curation-rationale) + - [Source Data](#source-data) + - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) + - [Who are the source language producers?](#who-are-the-source-language-producers) + - [Annotations](#annotations) + - [Annotation process](#annotation-process) + - [Who are the annotators?](#who-are-the-annotators) + - [Personal and Sensitive Information](#personal-and-sensitive-information) + - [Considerations for Using the Data](#considerations-for-using-the-data) + - [Social Impact of Dataset](#social-impact-of-dataset) + - [Discussion of Biases](#discussion-of-biases) + - [Other Known Limitations](#other-known-limitations) + - [Additional Information](#additional-information) + - [Dataset Curators](#dataset-curators) + - [Licensing Information](#licensing-information) + - [Citation Information](#citation-information) + - [Contributions](#contributions) ## Dataset Description @@ -94,25 +101,29 @@ The text in the dataset is in English and the associated BCP-47 code is `en`. An example from Books domain is shown below: ```json -{'ans_subj_score': 1.0, - 'answer_subj_level': 5, - 'domain': 'books', - 'human_ans_indices': '(1568, 1585)', - 'human_ans_spans': 'excellent writing', - 'is_ans_subjective': True, - 'is_ques_subjective': True, - 'item_id': '014104179X', - 'nn_asp': 'write', - 'nn_mod': 'outstanding', - 'q_review_id': '5680d7a8e25e322c08c0247547b397e8', - 'q_reviews_id': 'fccf9075ab3d3ee56ba71d04d900b786', - 'query_asp': 'write', - 'query_mod': 'intelligent', - 'ques_subj_score': 0.0, - 'question': 'How is the write?', - 'question_subj_level': 5, - 'review': 'This was a really tough book to get through. I only did because I thought the writing was outstanding. Tolz is a literary virtuoso. There are great positives in this book, from the overall originality of the novel; to the very moving and powerful climax; to an extraordinarily original and intricate plot; through Tolz\'s writing. But there are also aspects that irritate and almost made me give up halfway. These are:- the characters are impossible to like. This applies to Martin and Jasper Dean, both of whom are just too wierd and eccentric and self-important to care about. The review on the cover page comparing this novel to " A Confederacy of Dunces" does "Confederacy..." a disservice: Ignatius O Reilly is also wierd and eccentric and self important but he was comic and pathetic in a way that the Deans never manage to be. By the way, its not easy to like much any of the secondary characters either...- some plot twists are hard to handle, eg. Anouk\'s transformation from hippy into "one of the richest women in Australia";- while the book had a hugely entertaining first 100 or so pages and equally excellent final 100 pages, the middle was boring at times, irratating at others (where it seems Tolz wants to show he\'s read every book on philosophy ever written). The one exception here is the part involving bullying and suicides at Jasper\'s school, which is really really emotionally devastating - enough so to make one persist through the book in search of more of the same power (which does finally happen).3 stars therefore for exceptional power and excellent writing, versus some (rather lengthy) deeply irritating sections and unsympathetic characters. But I\'m very curious to see what Tolz will come up with next ANSWERNOTFOUND', - 'review_id': '8d63e311f8f6ab0ae17a6a883fdae5d7'} +{ + "answers": { + "ans_subj_score": [1.0], + "answer_start": [324], + "answer_subj_level": [2], + "is_ans_subjective": [True], + "text": ["This is a wonderfully written book"], + }, + "context": "While I would not recommend this book to a young reader due to a couple pretty explicate scenes I would recommend it to any adult who just loves a good book. Once I started reading it I could not put it down. I hesitated reading it because I didn't think that the subject matter would be interesting, but I was so wrong. This is a wonderfully written book.", + "domain": "books", + "id": "0255768496a256c5ed7caed9d4e47e4c", + "is_ques_subjective": False, + "nn_asp": "matter", + "nn_mod": "interesting", + "q_reviews_id": "a907837bafe847039c8da374a144bff9", + "query_asp": "part", + "query_mod": "fascinating", + "ques_subj_score": 0.0, + "question": "What are the parts like?", + "question_subj_level": 2, + "review_id": "a7f1a2503eac2580a0ebbc1d24fffca1", + "title": "0002007770", +} ``` ### Data Fields @@ -226,3 +237,7 @@ If you are using the dataset, please cite the following in your work: publisher = "Association for Computational Linguistics", } ``` + +### Contributions + +Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. \ No newline at end of file From 51efbbc38b822133b7359ea54bc083e87cd0411b Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Wed, 5 May 2021 14:07:53 +0200 Subject: [PATCH 14/21] Fix field description in README --- datasets/subjqa/README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 5fd33869ff2..6cffe6f3dac 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -130,25 +130,25 @@ An example from Books domain is shown below: Each domain and split consists of the following columns: -* ```domain```: The category/domain of the review (e.g., hotels, books, ...). +* ```title```: The id of the item/business discussed in the review. * ```question```: The question (written based on a query opinion). -* ```review```: The review (that mentions the neighboring opinion). -* ```human_ans_spans```: The span labeled by annotators as the answer. -* ```human_ans_indices```: The (character-level) start and end indices of the answer span highlighted by annotators. +* ```id```: A unique id assigned to the question-review pair. +* ```q_reviews_id```: A unique id assigned to all question-review pairs with a shared question. * ```question_subj_level```: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective). * ```ques_subj_score```: The subjectivity score of the question computed using the [TextBlob](https://textblob.readthedocs.io/en/dev/) package. +* ```context```: The review (that mentions the neighboring opinion). +* ```review_id```: A unique id associated with the review. +* ```answers.text```: The span labeled by annotators as the answer. +* ```answers.answer_start```: The (character-level) start index of the answer span highlighted by annotators. * ```is_ques_subjective```: A boolean subjectivity label derived from ```question_subj_level``` (i.e., scores below 4 are considered as subjective) -* ```answer_subj_level```: The subjectiviy level of the answer span (on a 1 to 5 scale with 5 being the most subjective). -* ```ans_subj_score```: The subjectivity score of the answer span computed usign the [TextBlob](https://textblob.readthedocs.io/en/dev/) package. -* ```is_ans_subjective```: A boolean subjectivity label derived from ```answer_subj_level``` (i.e., scores below 4 are considered as subjective) +* ```answers.answer_subj_level```: The subjectiviy level of the answer span (on a 1 to 5 scale with 5 being the most subjective). +* ```answers.ans_subj_score```: The subjectivity score of the answer span computed usign the [TextBlob](https://textblob.readthedocs.io/en/dev/) package. +* ```answers.is_ans_subjective```: A boolean subjectivity label derived from ```answer_subj_level``` (i.e., scores below 4 are considered as subjective) +* ```domain```: The category/domain of the review (e.g., hotels, books, ...). * ```nn_mod```: The modifier of the neighboring opinion (which appears in the review). * ```nn_asp```: The aspect of the neighboring opinion (which appears in the review). * ```query_mod```: The modifier of the query opinion (around which a question is manually written). * ```query_asp```: The aspect of the query opinion (around which a question is manually written). -* ```item_id```: The id of the item/business discussed in the review. -* ```review_id```: A unique id associated with the review. -* ```q_review_id```: A unique id assigned to the question-review pair. -* ```q_reviews_id```: A unique id assigned to all question-review pairs with a shared question. ### Data Splits From 01e4d7e9a522bf5006da367a8e1cba49b6ea5548 Mon Sep 17 00:00:00 2001 From: lewtun Date: Wed, 5 May 2021 21:54:20 +0200 Subject: [PATCH 15/21] Add dataset statistics and collection info to the README --- datasets/subjqa/README.md | 66 +++++++++++++++++++++++++-------------- 1 file changed, 43 insertions(+), 23 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 6cffe6f3dac..17fd2aba272 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -54,17 +54,17 @@ task_ids: ## Dataset Description -- **Homepage:** [Needs More Information] - **Repository:** https://github.com/lewtun/SubjQA - **Paper:** https://arxiv.org/abs/2004.14283 -- **Leaderboard:** [Needs More Information] - **Point of Contact:** [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) -### Dataset Summary +### Dataset Summary SubjQA is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly **10,000** questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a _subjectivity_ label by annotators. Questions such as _"How much does this product weigh?"_ is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity). -In short, SubjQA provides a setting to study how well extractive QA systems perform on finding answer that are less factual and to what extent modeling subjectivity can imporve thte performance of QA systems. +In short, SubjQA provides a setting to study how well extractive QA systems perform on finding answer that are less factual and to what extent modeling subjectivity can improve the performance of QA systems. + +_Note:_ Much of the information provided on this dataset card is taken from the README provided by the authors in their GitHub repository ([link](https://github.com/megagonlabs/SubjQA)). To load a domain with `datasets` you can run the following: @@ -75,20 +75,12 @@ from datasets import load_dataset dataset = load_dataset("subjqa", "books") ``` -#### Data collection -The SubjQA dataset is constructed based on publicly available review datasets. Specifically, the _movies_, _books_, _electronics_, and _grocery_ categories are constructed using reviews from the [Amazon Review dataset](http://jmcauley.ucsd.edu/data/amazon/links.html). The _TripAdvisor_ category, as the name suggests, is constructed using reviews from TripAdvisor which can be found [here](http://times.cs.uiuc.edu/~wang296/Data/). Finally, the _restaurants_ category is constructed using the [Yelp Dataset](https://www.yelp.com/dataset) which is also publicly available. +### Supported Tasks and Leaderboards -The process of constructing SubjQA is discussed in detail in our [paper](https://arxiv.org/abs/2004.14283). In a nutshell, the dataset construction consists of the following steps: +* `question-answering`: The dataset can be used to train a model for extractive question answering, which involves questions whose answer can be identified as a span of text in a review. Success on this task is typically measured by achieving a high Exact Match or F1 score. The BERT model that is first fine-tuned on SQuAD 2.0 and then further fine-tuned on SubjQA achieves the scores shown in the figure below. -1. First, all _opinions_ expressed in reviews are extracted. In the pipeline, each opinion is modeled as a (_modifier_, _aspect_) pair which is a pair of spans where the former describes the latter. (good, hotel), and (terrible, acting) are a few examples of extracted opinions. -2. Using Matrix Factorization techniques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion. -3. Annotators are then asked to write a question based on _query_ opinions. For instance given "good keyboard" as the query opinion, they might write "Is this keyboard any good?" -4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys". -5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span. - -### Supported Tasks and Leaderboards +![scores](https://user-images.githubusercontent.com/26859204/117199763-e02e1100-adea-11eb-9198-f3190329a588.png) -[Needs More Information] ### Languages @@ -98,7 +90,7 @@ The text in the dataset is in English and the associated BCP-47 code is `en`. ### Data Instances -An example from Books domain is shown below: +An example from `books` domain is shown below: ```json { @@ -106,13 +98,13 @@ An example from Books domain is shown below: "ans_subj_score": [1.0], "answer_start": [324], "answer_subj_level": [2], - "is_ans_subjective": [True], + "is_ans_subjective": [true], "text": ["This is a wonderfully written book"], }, "context": "While I would not recommend this book to a young reader due to a couple pretty explicate scenes I would recommend it to any adult who just loves a good book. Once I started reading it I could not put it down. I hesitated reading it because I didn't think that the subject matter would be interesting, but I was so wrong. This is a wonderfully written book.", "domain": "books", "id": "0255768496a256c5ed7caed9d4e47e4c", - "is_ques_subjective": False, + "is_ques_subjective": false, "nn_asp": "matter", "nn_mod": "interesting", "q_reviews_id": "a907837bafe847039c8da374a144bff9", @@ -152,19 +144,47 @@ Each domain and split consists of the following columns: ### Data Splits -Each domain consists of three splits: train, dev, and test. +The question-review pairs from each domain are split into training, development, and test sets. The table below shows the size of the dataset per each domain and split. + +| Domain | Train | Dev | Test | Total | +|-------------|-------|-----|------|-------| +| TripAdvisor | 1165 | 230 | 512 | 1686 | +| Restaurants | 1400 | 267 | 266 | 1683 | +| Movies | 1369 | 261 | 291 | 1677 | +| Books | 1314 | 256 | 345 | 1668 | +| Electronics | 1295 | 255 | 358 | 1659 | +| Grocery | 1124 | 218 | 591 | 1725 | + +Based on the subjectivity labels provided by annotators, one observes that 73% of the questions and 74% of the answers in the dataset are subjective. This provides a substantial number of subjective QA pairs as well as a reasonable number of factual questions to compare and constrast the performance of QA systems on each type of QA pairs. + +Finally, the next table summarizes the average length of the question, the review, and the highlighted answer span for each category. + +| Domain | Review Len | Question Len | Answer Len | % answerable | +|-------------|------------|--------------|------------|--------------| +| TripAdvisor | 187.25 | 5.66 | 6.71 | 78.17 | +| Restaurants | 185.40 | 5.44 | 6.67 | 60.72 | +| Movies | 331.56 | 5.59 | 7.32 | 55.69 | +| Books | 285.47 | 5.78 | 7.78 | 52.99 | +| Electronics | 249.44 | 5.56 | 6.98 | 58.89 | +| Grocery | 164.75 | 5.44 | 7.25 | 64.69 | ## Dataset Creation ### Curation Rationale -[Needs More Information] - ### Source Data #### Initial Data Collection and Normalization -[Needs More Information] +The SubjQA dataset is constructed based on publicly available review datasets. Specifically, the _movies_, _books_, _electronics_, and _grocery_ categories are constructed using reviews from the [Amazon Review dataset](http://jmcauley.ucsd.edu/data/amazon/links.html). The _TripAdvisor_ category, as the name suggests, is constructed using reviews from TripAdvisor which can be found [here](http://times.cs.uiuc.edu/~wang296/Data/). Finally, the _restaurants_ category is constructed using the [Yelp Dataset](https://www.yelp.com/dataset) which is also publicly available. + +The process of constructing SubjQA is discussed in detail in the [paper](https://arxiv.org/abs/2004.14283). In a nutshell, the dataset construction consists of the following steps: + +1. First, all _opinions_ expressed in reviews are extracted. In the pipeline, each opinion is modeled as a (_modifier_, _aspect_) pair which is a pair of spans where the former describes the latter. (good, hotel), and (terrible, acting) are a few examples of extracted opinions. +2. Using Matrix Factorization techniques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion. +3. Annotators are then asked to write a question based on _query_ opinions. For instance given "good keyboard" as the query opinion, they might write "Is this keyboard any good?" +4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys". +5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span. #### Who are the source language producers? @@ -240,4 +260,4 @@ If you are using the dataset, please cite the following in your work: ### Contributions -Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. \ No newline at end of file +Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. From 3f9664f7dc0b09fa315acc85c795b3e6df95b1fe Mon Sep 17 00:00:00 2001 From: Lewis Tunstall Date: Thu, 6 May 2021 09:25:40 +0200 Subject: [PATCH 16/21] Add info on dataset creation --- datasets/subjqa/README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 17fd2aba272..291202b3ce2 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -26,7 +26,6 @@ task_ids: - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - - [Data collection](#data-collection) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) @@ -58,7 +57,7 @@ task_ids: - **Paper:** https://arxiv.org/abs/2004.14283 - **Point of Contact:** [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) -### Dataset Summary +### Dataset Summary SubjQA is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly **10,000** questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a _subjectivity_ label by annotators. Questions such as _"How much does this product weigh?"_ is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity). @@ -157,7 +156,7 @@ The question-review pairs from each domain are split into training, development, Based on the subjectivity labels provided by annotators, one observes that 73% of the questions and 74% of the answers in the dataset are subjective. This provides a substantial number of subjective QA pairs as well as a reasonable number of factual questions to compare and constrast the performance of QA systems on each type of QA pairs. -Finally, the next table summarizes the average length of the question, the review, and the highlighted answer span for each category. +Finally, the next table summarizes the average length of the question, the review, and the highlighted answer span for each category. | Domain | Review Len | Question Len | Answer Len | % answerable | |-------------|------------|--------------|------------|--------------| @@ -172,6 +171,8 @@ Finally, the next table summarizes the average length of the question, the revie ### Curation Rationale +Most question-answering datasets like SQuAD and Natural Questions focus on answering questions over factual data such as Wikipedia and news articles. However, in domains like e-commerce the questions and answers are often _subjective_, that is, they depend on the personal experience of the users. For example, a customer on Amazon may ask "Is the sound quality any good?", which is more difficult to answer than a factiod question like "What is the capital of Australia?" These considerations motivate the creation of SubjQA as a tool to investigate the relationship between subjectivity and question-answering. + ### Source Data #### Initial Data Collection and Normalization @@ -188,7 +189,7 @@ The process of constructing SubjQA is discussed in detail in the [paper](https:/ #### Who are the source language producers? -[Needs More Information] +As described above, the source data for SubjQA is customer reviews of products and services on e-commerce websites like Amazon and TripAdvisor. ### Annotations From 8391c91957b445a31c2b8b4970c61c32ccbed141 Mon Sep 17 00:00:00 2001 From: lewtun Date: Thu, 6 May 2021 09:40:49 +0200 Subject: [PATCH 17/21] Add annotation process and social impact to README --- datasets/subjqa/README.md | 35 ++++++++++++++++++++++++++++------- 1 file changed, 28 insertions(+), 7 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 291202b3ce2..ae8cc14af64 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -187,6 +187,10 @@ The process of constructing SubjQA is discussed in detail in the [paper](https:/ 4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys". 5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span. +A visualisation of the data collection pipeline is shown in the image below. + +![preview](https://user-images.githubusercontent.com/26859204/117258393-3764cd80-ae4d-11eb-955d-aa971dbb282e.jpg) + #### Who are the source language producers? As described above, the source data for SubjQA is customer reviews of products and services on e-commerce websites like Amazon and TripAdvisor. @@ -195,11 +199,24 @@ As described above, the source data for SubjQA is customer reviews of products a #### Annotation process -[Needs More Information] +The generation of questions and answer span labels were obtained through the [Appen](https://appen.com/) platform. From the SubjQA paper: + +> The platform provides quality control by showing the workers 5 questions at a time, out of which one is labeled by the experts. A worker who fails to maintain 70% accuracy is kicked out by the platform and his judgements are ignored ... To ensure good quality labels, we paid each worker 5 cents per annotation. + +The instructions for generating a question are shown in the following figure: + +ques_gen + +Similarly, the interface for the answer span and subjectivity labelling tasks is shown below: + +![span_collection](https://user-images.githubusercontent.com/26859204/117259223-1fda1480-ae4e-11eb-9305-658ee6e3971d.png) + +As described in the SubjQA paper, the workers assign subjectivity scores (1-5) to each question and the selected answer span. They can also indicate if a question cannot be answered from the given review. + #### Who are the annotators? -[Needs More Information] +Workers on the Appen platform. ### Personal and Sensitive Information @@ -209,7 +226,7 @@ As described above, the source data for SubjQA is customer reviews of products a ### Social Impact of Dataset -[Needs More Information] +The SubjQA dataset can be used to develop question-answering systems that can provide better on-demand answers to e-commerce customers who are interested in subjective questions about products and services. ### Discussion of Biases @@ -223,13 +240,17 @@ As described above, the source data for SubjQA is customer reviews of products a ### Dataset Curators -[Needs More Information] +The people involved in creating the SubjQA dataset are the authors of the accompanying paper: + +* Johannes Bjerva1, Department of Computer Science, University of Copenhagen, Department of Computer Science, Aalborg University +* Nikita Bhutani, Megagon Labs, Mountain View +* Behzad Golshan, Megagon Labs, Mountain View +* Wang-Chiew Tan, Megagon Labs, Mountain View +* Isabelle Augenstein, Department of Computer Science, University of Copenhagen ### Licensing Information -The SubjQA dataset is provided "as-is", and its creators make no -represenntation as to its accuracy. Furthermore, the creators -have on obligation to maintain the dataset. +The SubjQA dataset is provided "as-is", and its creators make no representation as to its accuracy. The SubjQA dataset is constructed based on the following datasets and thus contains subsets of their data: * [Amazon Review Dataset](http://jmcauley.ucsd.edu/data/amazon/links.html) from UCSD From 4a1a4f3902dc9fb95d15d1cae7ae91b98de1148e Mon Sep 17 00:00:00 2001 From: lewtun Date: Fri, 7 May 2021 15:53:30 +0200 Subject: [PATCH 18/21] Update datasets/subjqa/README.md Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> --- datasets/subjqa/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index ae8cc14af64..a8d7b5c4527 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -171,7 +171,7 @@ Finally, the next table summarizes the average length of the question, the revie ### Curation Rationale -Most question-answering datasets like SQuAD and Natural Questions focus on answering questions over factual data such as Wikipedia and news articles. However, in domains like e-commerce the questions and answers are often _subjective_, that is, they depend on the personal experience of the users. For example, a customer on Amazon may ask "Is the sound quality any good?", which is more difficult to answer than a factiod question like "What is the capital of Australia?" These considerations motivate the creation of SubjQA as a tool to investigate the relationship between subjectivity and question-answering. +Most question-answering datasets like SQuAD and Natural Questions focus on answering questions over factual data such as Wikipedia and news articles. However, in domains like e-commerce the questions and answers are often _subjective_, that is, they depend on the personal experience of the users. For example, a customer on Amazon may ask "Is the sound quality any good?", which is more difficult to answer than a factoid question like "What is the capital of Australia?" These considerations motivate the creation of SubjQA as a tool to investigate the relationship between subjectivity and question-answering. ### Source Data From b1419ccdfe2e29992dd07a9fb1de7deb9fffa4b8 Mon Sep 17 00:00:00 2001 From: lewtun Date: Fri, 7 May 2021 15:54:54 +0200 Subject: [PATCH 19/21] Update datasets/subjqa/README.md Co-authored-by: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> --- datasets/subjqa/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index a8d7b5c4527..2e736a78e52 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -13,6 +13,9 @@ size_categories: - 1K Date: Fri, 7 May 2021 16:00:27 +0200 Subject: [PATCH 20/21] Replace eval with ast.literal_eval for safety! --- datasets/subjqa/subjqa.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/datasets/subjqa/subjqa.py b/datasets/subjqa/subjqa.py index 4417db1deff..642759c4046 100644 --- a/datasets/subjqa/subjqa.py +++ b/datasets/subjqa/subjqa.py @@ -17,6 +17,7 @@ electronics, TripAdvisor (i.e. hotels), and restaurants.""" +import ast import os import pandas as pd @@ -179,7 +180,7 @@ def _create_paragraphs(self, df): for k, v in id2question.items(): d = df.query(f"q_review_id == '{k}'").to_dict(orient="list") - answer_starts = [eval(a)[0] for a in d["human_ans_indices"]] + answer_starts = [ast.literal_eval(a)[0] for a in d["human_ans_indices"]] answer_meta = {k: v[0] for k, v in d.items() if k in self.answer_meta_columns} question_meta = {k: v[0] for k, v in d.items() if k in self.question_meta_columns} # Only fill answerable questions From 92651930a4c479886589d179b2f934f8b91200c6 Mon Sep 17 00:00:00 2001 From: Quentin Lhoest <42851186+lhoestq@users.noreply.github.com> Date: Mon, 10 May 2021 11:00:12 +0200 Subject: [PATCH 21/21] fix missing extended| prefix in source datasets tags --- datasets/subjqa/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/datasets/subjqa/README.md b/datasets/subjqa/README.md index 2e736a78e52..5c896afb47a 100644 --- a/datasets/subjqa/README.md +++ b/datasets/subjqa/README.md @@ -13,9 +13,9 @@ size_categories: - 1K