A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

Samuel Rönnqvist, Niko Schenk, Christian Chiarcos


Abstract
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.
Anthology ID:
P17-2040
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
256–262
Language:
URL:
https://aclanthology.org/P17-2040
DOI:
10.18653/v1/P17-2040
Bibkey:
Cite (ACL):
Samuel Rönnqvist, Niko Schenk, and Christian Chiarcos. 2017. A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 256–262, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations (Rönnqvist et al., ACL 2017)
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PDF:
https://preview.aclanthology.org/paclic-22-ingestion/P17-2040.pdf