Evaluating Compound Splitters Extrinsically with Textual Entailment

Glorianna Jagfeld, Patrick Ziering, Lonneke van der Plas


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P17-2010.pdf
Video:
 https://vimeo.com/234952906