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20202023
most citedEvaluating Distributional Distortion in Neural Language Modeling

4 citations · 8 across the 6 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CL2021

Systematic Generalization with Edge Transformers

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

Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neur…

cs.CL2021

Compositional Generalization in Dependency Parsing

Emily Goodwin, Siva Reddy, Timothy J. O'Donnell +1

Compositionality -- the ability to combine familiar units like words into novel phrases and sentences -- has been the focus of intense interest in artificial intelligence in recent…

cs.CL2021★ 1 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.CL2021

Linguistic Dependencies and Statistical Dependence

Jacob Louis Hoover, Alessandro Sordoni, Wenyu Du +1

Are pairs of words that tend to occur together also likely to stand in a linguistic dependency? This empirical question is motivated by a long history of literature in cognitive sc…

cs.CL2021

Characterizing Idioms: Conventionality and Contingency

Michaela Socolof, Jackie Chi Kit Cheung, Michael Wagner +1

Idioms are unlike most phrases in two important ways. First, the words in an idiom have non-canonical meanings. Second, the non-canonical meanings of words in an idiom are continge…