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Bridging Resolution: Making Sense of the State of the Art
- Source :
- NAACL-HLT
- Publication Year :
- 2021
- Publisher :
- Association for Computational Linguistics, 2021.
-
Abstract
- While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
- Subjects :
- Bridging (networking)
Computer science
business.industry
02 engineering and technology
Resolution (logic)
computer.software_genre
03 medical and health sciences
0302 clinical medicine
030221 ophthalmology & optometry
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
State (computer science)
business
computer
Natural language processing
Strengths and weaknesses
Anaphora (rhetoric)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Accession number :
- edsair.doi...........7eed32c014e4859b0dcbf994e1ec2333