activity
20202022
most citedEvaluating Distributional Distortion in Neural Language Modeling

4 citations · 6 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20224 cited

Evaluating Distributional Distortion in Neural Language Modeling

Benjamin LeBrun, Alessandro Sordoni, Timothy J. O'Donnell

A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions. Because of this novelty, a heavy-tail of rare events accounts for a s…

cs.CL20211 cited

Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention

Leon Bergen, Dzmitry Bahdanau, Timothy J. O'Donnell

We present a model that jointly learns the denotations of words together with their groundings using a truth-conditional semantics. Our model builds on the neurosymbolic approach o…

cs.CL2020

Recursive Top-Down Production for Sentence Generation with Latent Trees

Shawn Tan, Yikang Shen, Timothy J. O'Donnell +2

We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises o…

cs.CL20201 cited

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach

Wenyu Du, Zhouhan Lin, Yikang Shen +3

It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structu…

cs.CL2020

Probing Linguistic Systematicity

Emily Goodwin, Koustuv Sinha, Timothy J. O'Donnell

Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicity; generalizing such that units like words make con…