Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts

Yasuhide Miura, Ryuji Kano, Motoki Taniguchi, Tomoki Taniguchi, Shotaro Misawa, Tomoko Ohkuma


Abstract
We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.
Anthology ID:
C18-1322
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3806–3818
Language:
URL:
https://aclanthology.org/C18-1322
DOI:
Bibkey:
Cite (ACL):
Yasuhide Miura, Ryuji Kano, Motoki Taniguchi, Tomoki Taniguchi, Shotaro Misawa, and Tomoko Ohkuma. 2018. Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3806–3818, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts (Miura et al., COLING 2018)
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PDF:
https://preview.aclanthology.org/naacl24-info/C18-1322.pdf