An assential aspect of structured predic- tion is the evaluation of an output struc- ture against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effec- tive loss functions. In this paper, we trade off exact computation for enabling the use of more complex loss functions for coreference resolution (CR). Most note- worthily, we show that such functions can be (i) automatically learned also from controversial but commonly accepted CR measures, e.g., MELA, and (ii) success- fully used in learning algorithms. The ac- curate model comparison on the standard CoNLL–2012 setting shows the benefit of more expressive loss for Arabic and En- glish data.

Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures / Haponchyk, Iryna; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 1018-1028. [10.18653/v1/P17-1094]

Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures

Iryna Haponchyk;Alessandro Moschitti
2017

Abstract

An assential aspect of structured predic- tion is the evaluation of an output struc- ture against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effec- tive loss functions. In this paper, we trade off exact computation for enabling the use of more complex loss functions for coreference resolution (CR). Most note- worthily, we show that such functions can be (i) automatically learned also from controversial but commonly accepted CR measures, e.g., MELA, and (ii) success- fully used in learning algorithms. The ac- curate model comparison on the standard CoNLL–2012 setting shows the benefit of more expressive loss for Arabic and En- glish data.
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers
Stroudsburg, Pennsylvania
Association for Computational Linguistics ACL
978-1-945626-75-3
Haponchyk, Iryna; Moschitti, Alessandro
Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures / Haponchyk, Iryna; Moschitti, Alessandro. - ELETTRONICO. - (2017), pp. 1018-1028. [10.18653/v1/P17-1094]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/195373
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