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 language | English |
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Title of host publication | LREC 2018 - 11th International Conference on Language Resources and Evaluation |
Editors | Hitoshi 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 |
Publisher | European Language Resources Association (ELRA) |
Pages | 1643-1648 |
Number of pages | 6 |
ISBN (Electronic) | 9791095546009 |
Publication status | Published - 1 Jan 2019 |
MoE publication type | A4 Article in a conference publication |
Event | International Conference on Language Resources and Evaluation - Miyazaki, Japan Duration: 7 May 2018 → 12 May 2018 Conference number: 11 |
Conference
Conference | International Conference on Language Resources and Evaluation |
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Abbreviated title | LREC |
Country | Japan |
City | Miyazaki |
Period | 07/05/2018 → 12/05/2018 |
Keywords
- Decomposable attention model
- Discourse connectives
- Discourse relation prediction