125 citations · 584 across the 45 of their papers we have counts for
7 papers · 1 filter
Improving Compositional Generalization with Latent Structure and Data Augmentation
Linlu Qiu, Peter Shaw, Panupong Pasupat +4
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has…
Frequency Effects on Syntactic Rule Learning in Transformers
Jason Wei, Dan Garrette, Tal Linzen +1
Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract…
NOPE: A Corpus of Naturally-Occurring Presuppositions in English
Alicia Parrish, Sebastian Schuster, Alex Warstadt +5
Understanding language requires grasping not only the overtly stated content, but also making inferences about things that were left unsaid. These inferences include presupposition…
Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Matthew Finlayson, Aaron Mueller, Sebastian Gehrmann +3
Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the…
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction
Shauli Ravfogel, Grusha Prasad, Tal Linzen +1
When language models process syntactically complex sentences, do they use their representations of syntax in a manner that is consistent with the grammar of the language? We propos…
Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark
Nouha Dziri, Hannah Rashkin, Tal Linzen +1
Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress…
Does Putting a Linguist in the Loop Improve NLU Data Collection?
Alicia Parrish, William Huang, Omar Agha +7
Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples durin…