added details (#380)

_pages/reading-group.md

@@ -24,7 +24,7 @@ For the Fall 2024 semester, the reading group will meet on Fridays at 11:30AM (w
 | September 27th @ 11:30 AM | Steven Koniaev, Nathan Zeweniuk | Intern Presentations | [click here]({% link _posts/reading-group/fall-2024/2024-09-27-intern-presentation-2.md %}) |
 | October 1st to 3rd | Mila NLP Workshop | [Register here](https://mila.quebec/en/event/workshop-nlp-in-the-era-of-generative-ai-cognitive-sciences-and-societal-transformation) | | |
 | October 11th @ 11:00 AM | Tu Vu | Efficient Model Development in the Era of Large Language Models | [click here]({% link _posts/reading-group/fall-2024/2024-10-11-tu-vu.md %}) |
-| October 18th @ 11:30 AM | Eva Portelance | *TBA* | *TBA* |
+| October 18th @ 11:30 AM | Eva Portelance | Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models | [click here]({% link _posts/reading-group/fall-2024/2024-10-18-eva-portelance.md %}) |
 | November 1st @ 11:30 AM | Bang Liu | *TBA* | *TBA* |
 | November 8th @ 11:30 AM | Boyuan Zheng | *TBA* | *TBA* |
 | November 12th to 16th | **EMNLP 2024** | | |

_posts/reading-group/fall-2024/2024-10-18-eva-portelance.md

@@ -0,0 +1,38 @@
+---
+title: "Reframing linguistic bootstrapping as joint inference using visually-grounded grammar induction models"
+venue: HEC Montreal
+names: Eva Portelance
+author: Eva Portelance
+tags:
+- NLP RG
+categories:
+    - Reading-Group
+    - Fall-2024
+layout: archive
+classes:
+    - wide
+    - no-sidebar
+---
+
+*{{ page.names }}*
+
+**{{ page.venue }}**
+
+{% include display-publication-links.html pub=page %}
+
+The [NLP Reading Group]({% link _pages/reading-group.md %}) is excited to host [Eva Portelance](https://evaportelance.github.io/), an assistant professor at HEC Montreal, who will be speaking **in person** in Auditorium 2 at 11:30 AM on Friday October 18th about **joint learning in language acquisition**.
+
+
+## Talk Description
+
+Semantic and syntactic bootstrapping posit that children use their prior knowledge of one linguistic  domain, say syntactic relations, to help later acquire another, such as the meanings of new words. Empirical results supporting both theories may tempt us to believe that these are different learning strategies, where one may precede the other. Here, we argue that they are instead both contingent on a more general  learning strategy for language acquisition: joint learning. Using a series of neural visually-grounded grammar induction models, we demonstrate that both syntactic and semantic bootstrapping effects are strongest when syntax and semantics are learnt simultaneously. Joint learning results in better grammar induction, realistic lexical category learning, and better interpretations of novel sentence and verb  meanings. Joint learning makes language acquisition easier for learners by mutually constraining the hypotheses spaces for both syntax and semantics. Studying the dynamics of joint inference over many input sources and modalities represents an important new direction for language modeling and learning research in both cognitive sciences and AI, as it may help us explain how language can be acquired in more constrained learning settings.
+
+## Speaker Bio
+
+Eva Portelance is an Assistant Professor of machine learning in the Department of decision sciences at HEC Montréal and a member of Mila — Québec Artificial Intelligence Institute. Her research intersects AI and cognitive science; She is interested in understanding how both humans and machines learn to understand language and reason about complex problems. Previously, she was a postdoc at Mila and McGill University with Timothy J. O'Donnell and Siva Reddy. Her Ph.D. was in computational/cognitive linguistics from Stanford University, where she worked with Dan Jurafsky and Mike C. Frank.
+
+## Logistics
+
+Date: October 18th<br>
+Time: 11:30AM <br>
+Location: Auditorium 2 or via Zoom (See email)