A recurrent neural model with attention for the recognition of Chinese implicit discourse relations

  • We introduce an attention-based Bi-LSTM for Chinese implicit discourse relations and demonstrate that modeling argument pairs as a joint sequence can outperform word order-agnostic approaches. Our model benefits from a partial sampling scheme and is conceptually simple, yet achieves state-of-the-art performance on the Chinese Discourse Treebank. We also visualize its attention activity to illustrate the model’s ability to selectively focus on the relevant parts of an input sequence.

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Metadaten
Author:Samuel Rönnqvist, Niko Schenk, Christian ChiarcosORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1041110
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/104111
ISBN:978-1-945626-76-0OPAC
Parent Title (English):Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), July 30 - August 4, 2017, Vancouver, Canada
Publisher:Association for Computational Linguistics
Place of publication:Stroudsburg, PA
Editor:Chris Callison-Burch, Regina Barzilay, Min-Yen Kan, Wei Lu, Sameer Singh, Margaret Mitchell
Type:Conference Proceeding
Language:English
Year of first Publication:2017
Publishing Institution:Universität Augsburg
Release Date:2023/05/10
First Page:256
Last Page:262
DOI:https://doi.org/10.18653/v1/P17-2040
Institutes:Philologisch-Historische Fakultät
Philologisch-Historische Fakultät / Angewandte Computerlinguistik
Philologisch-Historische Fakultät / Angewandte Computerlinguistik / Lehrstuhl für Angewandte Computerlinguistik (ACoLi)
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)

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