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DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |