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Event-centric question answering via contrastive learning and invertible event transformation
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Lu, Junru, Tan, Xingwei, Pergola, Gabriele, Gui, Lin and He, Yulan (2022) Event-centric question answering via contrastive learning and invertible event transformation. In: EMNLP 2022, Abu Dhabi, United Arab Emirates, 7-11 Dec 2022. Published in: Findings of the Association for Computational Linguistics: EMNLP 2022 pp. 2377-2389.
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WRAP-Event-centric-question-answering-contrastive-learning-transformation-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2465Kb) | Preview |
Official URL: https://aclanthology.org/2022.findings-emnlp.176
Abstract
Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.
| Item Type: | Conference Item (Paper) | |||||||||||||||
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| Subjects: | P Language and Literature > P Philology. Linguistics Q Science > Q Science (General) 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): | Computational linguistics , Artificial intelligence , Machine learning , Natural language processing (Computer science) | |||||||||||||||
| Journal or Publication Title: | Findings of the Association for Computational Linguistics: EMNLP 2022 | |||||||||||||||
| Publisher: | Association for Computational Linguistics | |||||||||||||||
| Place of Publication: | Abu Dhabi, United Arab Emirates | |||||||||||||||
| Official Date: | December 2022 | |||||||||||||||
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| Page Range: | pp. 2377-2389 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Date of first compliant deposit: | 20 February 2023 | |||||||||||||||
| Date of first compliant Open Access: | 20 February 2023 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | |||||||||||||||
| Title of Event: | EMNLP 2022 | |||||||||||||||
| Type of Event: | Conference | |||||||||||||||
| Location of Event: | Abu Dhabi, United Arab Emirates | |||||||||||||||
| Date(s) of Event: | 7-11 Dec 2022 |
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