Papers by Jacqueline He
Can Rationalization Improve Robustness? (2022.naacl-main)
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| Challenge: | Existing models that generate rationales before making predictions can ignore noise or adversarially added text by simply masking it out of the generated rationale. |
| Approach: | They propose to use a 'rationalizethen-predict' framework to generate subsets of input to generate rationales and then make predictions using them. |
| Outcome: | The proposed models improve robustness over AddText attacks while struggling in certain scenarios. |
MABEL: Attenuating Gender Bias using Textual Entailment Data (2022.emnlp-main)
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| Challenge: | Existing methods for mitigating gender bias in language models are insufficient or inconsistent. |
| Approach: | They propose a method for attenuating gender bias using entailment labels . they use a contrastive learning objective on counterfactually augmented enanglement pairs . |
| Outcome: | The proposed method outperforms previous task-agnostic debiasing approaches on intrinsic and extrinsic metrics and preserves task performance after fine-tuning on downstream tasks. |
Challenges in Context-Aware Neural Machine Translation (2023.emnlp-main)
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| Challenge: | despite well-reasoned intuitions, most context-aware neural machine translation models show only modest improvements over sentence-level systems. |
| Approach: | They propose a more realistic setting for document-level translation called paragraph-to-paragraph (PARA2PARA) they collect a dataset of Chinese-English novels to promote future research . |
| Outcome: | The proposed model improves translation quality across document-level metrics and discourse phenomena. |