Attitude detection for one-round conversation: Jointly extracting target-polarity pairs

Zhaohao Zeng, Pingping Lin, Ruihua Song, Tetsuya Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.

Original languageEnglish
Title of host publicationWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages285-293
Number of pages9
ISBN (Electronic)9781450359405
DOIs
Publication statusPublished - 2019 Jan 30
Event12th ACM International Conference on Web Search and Data Mining, WSDM 2019 - Melbourne, Australia
Duration: 2019 Feb 112019 Feb 15

Publication series

NameWSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining

Conference

Conference12th ACM International Conference on Web Search and Data Mining, WSDM 2019
CountryAustralia
CityMelbourne
Period19/2/1119/2/15

Keywords

  • Attitude detection
  • Chatbot
  • Conversation
  • Sentiment analysis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

Cite this

Zeng, Z., Lin, P., Song, R., & Sakai, T. (2019). Attitude detection for one-round conversation: Jointly extracting target-polarity pairs. In WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 285-293). (WSDM 2019 - Proceedings of the 12th ACM International Conference on Web Search and Data Mining). Association for Computing Machinery, Inc. https://doi.org/10.1145/3289600.3291038