I do not disagree: leveraging monolingual alignment to detect disagreement in dialogue

Gokcen, Ajda;de Marneffe, Marie-Catherine
(2015) Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language — Location: Beijing, China (July.2015)

Files

P15-2016.pdf
  • Open Access
  • Adobe PDF
  • 156.79 KB
Download

Details

Authors
Abstract
A wide array of natural dialogue discourse can be found on the internet. Previous attempts to automatically determine disagreement between interlocutors in such dialogue have mostly relied on n-gram and grammatical dependency features taken from respondent text. Agreement-disagreement classifiers built upon these baseline features tend to do poorly, yet have proven difficult to improve upon. Using the Internet Argument Corpus, which comprises quote and response post pairs taken from an online debate forum with human-annotated agreement scoring, we introduce semantic environment features derived by comparing quote and response sentences which align well. We show that this method improves classifier accuracy relative to the baseline method namely in the retrieval of disagreeing pairs, which improves from 69% to 77%.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Gokcen, A., & de Marneffe, M.-C. (2015). I do not disagree: leveraging monolingual alignment to detect disagreement in dialogue. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2015). Published. Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language, Beijing, China. https://doi.org/10.3115/v1/p15-2016 (Original work published 2015)