Abstract
Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics Findings of ACL |
Subtitle of host publication | EMNLP 2020 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 1491-1505 |
Number of pages | 15 |
ISBN (Electronic) | 9781952148903 |
State | Published - 2020 |
Externally published | Yes |
Event | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Publication series
Name | Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 |
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Conference
Conference | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 |
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City | Virtual, Online |
Period | 16/11/20 → 20/11/20 |
Bibliographical note
Publisher Copyright:©2020 Association for Computational Linguistics
Funding
We would like to thank the members of the IE@Technion NLP group and Roee Aharoni, for their valuable feedback and advice. Roi Reichart was partially funded by ISF personal grant No. 1625/18.
Funders | Funder number |
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Israel Science Foundation | 1625/18 |