Papers by J. Edward Hu
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (D18-1)
Copied to clipboard
Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme
| Challenge: | a plethora of new natural language inference datasets has been created in recent years . however, these datasets do not provide clear insight into what type of reasoning or inference a model may be performing. |
| Approach: | They propose to recast 13 existing natural language inference datasets into a common structure. |
| Outcome: | The proposed datasets provide insight into how well a sentence representation captures distinct types of reasoning. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
Copied to clipboard
J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
Iterative Paraphrastic Augmentation with Discriminative Span Alignment (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing datasets can be expanded or created using a small, manually produced seed corpus. |
| Approach: | They propose a paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrases and discriminative span alignment. |
| Outcome: | The proposed approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. |