Papers by Damien Jose
Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
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Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
| Approach: | They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited. |
| Outcome: | The proposed NER systems outperform existing methods on few-shot and training-free settings. |
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations (2022.findings-acl)
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| Challenge: | Dense retrieval (DR) methods first encode texts into a dense embedding space and then conduct text retrieval using efficient nearest neighbor search. |
| Approach: | They propose Momentum adversarial Domain Invariant Representation learning to train a domain classifier that distinguishes source versus target domains and adversarially updates the DR encoder to learn domain invariant representations. |
| Outcome: | The proposed method outperforms baselines on 10+ ranking datasets collected in the BEIR benchmark in the zero-shot setting, with more than 10% relative gains on datasets with enough sensitivity for DR models’ evaluation. |