Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Learning scientific document representations can be substantially improved through contrastive learning objectives, where the challenge lies in creating positive and negative training samples that encode the desired similarity semantics. Prior work relies on discrete citation relations to generate contrast samples. However, discrete citations enforce a hard cut-off to similarity. This is counter-intuitive to similarity-based learning and ignores that scientific papers can be very similar despite lacking a direct citation - a core problem of finding related research. Instead, we use controlled nearest neighbor sampling over citation graph embeddings for contrastive learning. This control allows us to learn continuous similarity, to sample hard-to-learn negatives and positives, and also to avoid collisions between negative and positive samples by controlling the sampling margin between them. The resulting method SciNCL outperforms the state-of-the-art on the SciDocs benchmark. Furthermore, we demonstrate that it can train (or tune) language models sample-efficiently and that it can be combined with recent training-efficient methods. Perhaps surprisingly, even training a general-domain language model this way outperforms baselines pretrained in-domain.
Originalsprog | Engelsk |
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Titel | Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing |
Forlag | Association for Computational Linguistics (ACL) |
Publikationsdato | 2022 |
Sider | 11670–11688 |
Status | Udgivet - 2022 |
Begivenhed | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates Varighed: 7 dec. 2022 → 11 dec. 2022 |
Konference
Konference | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 |
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Land | United Arab Emirates |
By | Abu Dhabi |
Periode | 07/12/2022 → 11/12/2022 |
Links
- https://aclanthology.org/2022.emnlp-main.802/
Forlagets udgivne version
ID: 341060672