139 citations · 139 across the 6 of their papers we have counts for
12 papers
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…