Pauli Xu
2018
End-to-End Graph-Based TAG Parsing with Neural Networks
Jungo Kasai
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Robert Frank
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Pauli Xu
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William Merrill
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Owen Rambow
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs. Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points. The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems. We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery. This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.
2017
TAG Parser Evaluation using Textual Entailments
Pauli Xu
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Robert Frank
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Jungo Kasai
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Owen Rambow
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms
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