Abstract
Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation. We explore a novel way for the extrinsic evaluation of compound splitters, namely recognizing textual entailment. Compound splitting has great potential for this novel task that is both transparent and well-defined. Moreover, we show that it addresses certain aspects that are either ignored in intrinsic evaluations or compensated for by taskinternal mechanisms in statistical machine translation. We show significant improvements using different compound splitting methods on a German textual entailment dataset.- Anthology ID:
- P17-2010
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–63
- Language:
- URL:
- https://aclanthology.org/P17-2010
- DOI:
- 10.18653/v1/P17-2010
- Cite (ACL):
- Glorianna Jagfeld, Patrick Ziering, and Lonneke van der Plas. 2017. Evaluating Compound Splitters Extrinsically with Textual Entailment. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 58–63, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Compound Splitters Extrinsically with Textual Entailment (Jagfeld et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/P17-2010.pdf