139 citations · 161 across the 11 of their papers we have counts for
13 papers
Evaluating Paraphrastic Robustness in Textual Entailment Models
Dhruv Verma, Yash Kumar Lal, Shreyashee Sinha +2
We present PaRTE, a collection of 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate whether models are robust to paraphrasing. We posit that if RTE models un…
Discovering changes in birthing narratives during COVID-19
Daphna Spira, Noreen Mayat, Caitlin Dreisbach +1
We investigate whether, and if so how, birthing narratives written by new parents on Reddit changed during COVID-19. Our results indicate that the presence of family members signif…
Figurative Language in Recognizing Textual Entailment
Tuhin Chakrabarty, Debanjan Ghosh, Adam Poliak +1
We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative…
Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets
Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach +1
We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social Media Mining for Health Applications (SMM4H) shared tasks. Our system is based on fine-tuning Distill- BE…
Probing Neural Language Models for Human Tacit Assumptions
Nathaniel Weir, Adam Poliak, Benjamin Van Durme
Humans carry stereotypic tacit assumptions (STAs) (Prince, 1978), or propositional beliefs about generic concepts. Such associations are crucial for understanding natural language.…
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Yonatan Belinkov, Adam Poliak, Stuart M. Shieber +2
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious…
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
Yonatan Belinkov, Adam Poliak, Stuart M. Shieber +2
Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise e…
What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen +8
Contextualized representation models such as ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a diverse array of downst…
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Najoung Kim, Roma Patel, Adam Poliak +9
We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to t…
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
Adam Poliak, Yonatan Belinkov, James Glass +1
We propose a process for investigating the extent to which sentence representations arising from neural machine translation (NMT) systems encode distinct semantic phenomena. We use…
Hypothesis Only Baselines in Natural Language Inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar +2
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI). Especially when an NLI dataset assumes inference is occurring based purely on the relationshi…
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Adam Poliak, Aparajita Haldar, Rachel Rudinger +4
We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types o…
Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles
Francis Ferraro, Adam Poliak, Ryan Cotterell +1
We study how different frame annotations complement one another when learning continuous lexical semantics. We learn the representations from a tensorized skip-gram model that cons…