Abstract
We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-document similarity ranking has been studied, most modern methods are limited to relatively short documents or rely on the existence of “ground-truth” similarity labels. Yet, in most common real-world cases, similarity ranking is an unsupervised problem as similarity labels are unavailable. Moreover, an ideal model should not be restricted by documents' length. Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length. Importantly, SDR can be effectively applied to extremely long documents, exceeding the 4, 096 maximal token limit of Longformer. Extensive evaluations on large documents datasets show that SDR significantly outperforms its alternatives across all metrics. To accelerate future research on unlabeled long document similarity ranking, and as an additional contribution to the community, we herein publish two human-annotated test-sets of long documents similarity evaluation. The SDR code and datasets are publicly available.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics |
Subtitle of host publication | ACL-IJCNLP 2021 |
Editors | Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 3088-3098 |
Number of pages | 11 |
ISBN (Electronic) | 9781954085541 |
State | Published - 2021 |
Externally published | Yes |
Event | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online Duration: 1 Aug 2021 → 6 Aug 2021 |
Publication series
Name | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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Conference
Conference | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 |
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City | Virtual, Online |
Period | 1/08/21 → 6/08/21 |
Bibliographical note
Publisher Copyright:© 2021 Association for Computational Linguistics