13 citations · 25 across the 3 of their papers we have counts for
8 papers
Measuring and Reducing Gendered Correlations in Pre-trained Models
Kellie Webster, Xuezhi Wang, Ian Tenney +6
Pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode artifacts undesired in many applications, such as professions…
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Kellie Webster, Emily Pitler
Machine translation systems with inadequate document understanding can make errors when translating dropped or neutral pronouns into languages with gendered pronouns (e.g., English…
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Junghyun Min, R. Thomas McCoy, Dipanjan Das +2
Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of…
New Protocols and Negative Results for Textual Entailment Data Collection
Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares +1
Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing…
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Daniel Andor, Luheng He, Kenton Lee +1
Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We e…
Synthetic QA Corpora Generation with Roundtrip Consistency
Chris Alberti, Daniel Andor, Emily Pitler +2
We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ens…