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]
Titolo: | Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures | |
Autori: | Haponchyk, Iryna; Moschitti, Alessandro | |
Autori Unitn: | ||
Titolo del volume contenente il saggio: | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers | |
Luogo di edizione: | Stroudsburg, Pennsylvania | |
Casa editrice: | Association for Computational Linguistics ACL | |
Anno di pubblicazione: | 2017 | |
ISBN: | 978-1-945626-75-3 | |
Handle: | http://hdl.handle.net/11572/195373 | |
Citazione: | 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] | |
Appare nelle tipologie: | 02.1 Saggio su volume miscellaneo o Capitolo di libro (Essay or Book Chapter) |
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