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Extracting event temporal relations via hyperbolic geometry
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Tan, Xingwei, Pergola, Gabriele and He, Yulan (2021) Extracting event temporal relations via hyperbolic geometry. In: The 2021 Conference on Empirical Methods in Natural Language Processing, Online ; Punta Cana, Dominican Republic, 7–11 Nov 2021. Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing pp. 8065-8077. doi:10.18653/v1/2021.emnlp-main.636
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WRAP-extracting-event-temporal-relations-via-hyperbolic-geometry-Tan-2021.pdf - Accepted Version - Requires a PDF viewer. Download (3298Kb) | Preview |
Official URL: http://dx.doi.org/10.18653/v1/2021.emnlp-main.636
Abstract
Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics. Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces.
| Item Type: | Conference Item (Paper) | ||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Natural language processing (Computer science) , Geometry, Hyperbolic, Computational linguistics , Neural networks (Computer science) , Machine learning, Learning models (Stochastic processes) | ||||||||||||
| Journal or Publication Title: | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing | ||||||||||||
| Publisher: | Association for Computational Linguistics | ||||||||||||
| Official Date: | November 2021 | ||||||||||||
| Dates: |
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| Page Range: | pp. 8065-8077 | ||||||||||||
| DOI: | 10.18653/v1/2021.emnlp-main.636 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Date of first compliant deposit: | 15 September 2021 | ||||||||||||
| Date of first compliant Open Access: | 16 September 2021 | ||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||||||||
| Title of Event: | The 2021 Conference on Empirical Methods in Natural Language Processing | ||||||||||||
| Type of Event: | Conference | ||||||||||||
| Location of Event: | Online ; Punta Cana, Dominican Republic | ||||||||||||
| Date(s) of Event: | 7–11 Nov 2021 | ||||||||||||
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