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Measuring and Reducing Gendered Correlations in Pre-trained Models
Pre-trained models have revolutionized natural language understanding. H...
10/12/2020 ∙ by Kellie Webster, et al. ∙
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Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation
Machine translation systems with inadequate document understanding can m...
06/16/2020 ∙ by Kellie Webster, et al. ∙
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Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Pretrained neural models such as BERT, when fine-tuned to perform natura...
04/24/2020 ∙ by Junghyun Min, et al. ∙
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Collecting Entailment Data for Pretraining: New Protocols and Negative Results
Textual entailment (or NLI) data has proven useful as pretraining data f...
04/24/2020 ∙ by Samuel R. Bowman, et al. ∙
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Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension
Reading comprehension models have been successfully applied to extractiv...
08/31/2019 ∙ by Daniel Andor, et al. ∙
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Synthetic QA Corpora Generation with Roundtrip Consistency
We introduce a novel method of generating synthetic question answering c...
06/12/2019 ∙ by Chris Alberti, et al. ∙
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Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
The rise of neural networks, and particularly recurrent neural networks,...
05/21/2018 ∙ by Bernd Bohnet, et al. ∙
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Natural Language Processing with Small Feed-Forward Networks
We show that small and shallow feed-forward neural networks can achieve ...
08/01/2017 ∙ by Jan A. Botha, et al. ∙
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