Papers by Oliver Wang
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding (2023.emnlp-main)
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Steven Wang, Antoine Scardigli, Leonard Tang, Wei Chen, Dmitry Levkin, Anya Chen, Spencer Ball, Thomas Woodside, Oliver Zhang, Dan Hendrycks
| Challenge: | Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
| Approach: | They propose a Merger Agreement Understanding Dataset with over 39,000 examples and over 47,000 annotations. |
| Outcome: | The Merger Agreement Understanding Dataset (MAUD) is an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study. |
Localizing Moments in Video with Temporal Language (D18-1)
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| Challenge: | a novel model for localizing moments in a longer video using natural language queries is challenging . moment localization is similar to other language and vision tasks, but it offers an interesting opportunity to model temporal dependencies and reasoning in text. |
| Approach: | They propose a model that explicitly reasons about different temporal segments in a video . their dataset includes a dataset with real videos and template sentences . |
| Outcome: | The proposed model explicitly reasons about different temporal segments in a video . it shows that temporal context is important for localizing phrases which include temporal language . |