2021
ACL
ACL 2021
End-to-End AMR Coreference Resolution
Abstract
AbstractAlthough parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
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Interdisciplinary Bridge
— Artificial Intelligence and Natural Language Processing
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Keyword Pioneer
— multi-sentence representation
Authors
Topics
Natural Language Processing > Understanding > Coreference Resolution
Natural Language Processing > Understanding > Parsing
Natural Language Processing > Generation > Summarization
Artificial Intelligence > Core AI > Knowledge Representation
Natural Language Processing > Applications > Semantic Parsing