Automatic prediction of discourse connectives

Eric Malmi, Daniele Pighin, Sebastian Krause, Mikhail Kozhevnikov

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

6 Citations (Scopus)

Abstract

Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from the Web. We formulate the task of discourse connective prediction and release a dataset of 2.9M sentence pairs separated by discourse connectives for this task. Then, we evaluate the hardness of the task for human raters, apply a recently proposed decomposable attention (DA) model to this task and observe that the automatic predictor has a higher F1 than human raters (32 vs. 30). Nevertheless, under specific conditions the raters still outperform the DA model, suggesting that there is headroom for future improvements.

Original languageEnglish
Title of host publicationLREC 2018 - 11th International Conference on Language Resources and Evaluation
EditorsHitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga
PublisherEuropean Language Resources Association (ELRA)
Pages1643-1648
Number of pages6
ISBN (Electronic)9791095546009
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Language Resources and Evaluation - Miyazaki, Japan
Duration: 7 May 201812 May 2018
Conference number: 11

Conference

ConferenceInternational Conference on Language Resources and Evaluation
Abbreviated titleLREC
CountryJapan
CityMiyazaki
Period07/05/201812/05/2018

Keywords

  • Decomposable attention model
  • Discourse connectives
  • Discourse relation prediction

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