LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test
Michael Bugert, Yevgeniy Puzikov, Andreas Rücklé, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez-Cámara, Daniil Sorokin, Maxime Peyrard, Iryna Gurevych
Abstract
The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.- Anthology ID:
- W17-0908
- Volume:
- Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Venue:
- LSDSem
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 56–61
- Language:
- URL:
- https://aclanthology.org/W17-0908
- DOI:
- 10.18653/v1/W17-0908
- Cite (ACL):
- Michael Bugert, Yevgeniy Puzikov, Andreas Rücklé, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez-Cámara, Daniil Sorokin, Maxime Peyrard, and Iryna Gurevych. 2017. LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test. In Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pages 56–61, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test (Bugert et al., LSDSem 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W17-0908.pdf
- Code
- UKPLab/lsdsem2017-story-cloze
- Data
- ROCStories