@inproceedings{apidianaki-etal-2018-automated,
title = "Automated Paraphrase Lattice Creation for {H}y{TER} Machine Translation Evaluation",
author = "Apidianaki, Marianna and
Wisniewski, Guillaume and
Cocos, Anne and
Callison-Burch, Chris",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2077",
doi = "10.18653/v1/N18-2077",
pages = "480--485",
abstract = "We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions. The original HyTER metric relied on hand-crafted paraphrase networks which restricted its applicability to new data. We test, for the first time, HyTER with automatically built paraphrase lattices. We show that although the metric obtains good results on small and carefully curated data with both manually and automatically selected substitutes, it achieves medium performance on much larger and noisier datasets, demonstrating the limits of the metric for tuning and evaluation of current MT systems.",
}
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<abstract>We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions. The original HyTER metric relied on hand-crafted paraphrase networks which restricted its applicability to new data. We test, for the first time, HyTER with automatically built paraphrase lattices. We show that although the metric obtains good results on small and carefully curated data with both manually and automatically selected substitutes, it achieves medium performance on much larger and noisier datasets, demonstrating the limits of the metric for tuning and evaluation of current MT systems.</abstract>
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%0 Conference Proceedings
%T Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation
%A Apidianaki, Marianna
%A Wisniewski, Guillaume
%A Cocos, Anne
%A Callison-Burch, Chris
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F apidianaki-etal-2018-automated
%X We propose a variant of a well-known machine translation (MT) evaluation metric, HyTER (Dreyer and Marcu, 2012), which exploits reference translations enriched with meaning equivalent expressions. The original HyTER metric relied on hand-crafted paraphrase networks which restricted its applicability to new data. We test, for the first time, HyTER with automatically built paraphrase lattices. We show that although the metric obtains good results on small and carefully curated data with both manually and automatically selected substitutes, it achieves medium performance on much larger and noisier datasets, demonstrating the limits of the metric for tuning and evaluation of current MT systems.
%R 10.18653/v1/N18-2077
%U https://aclanthology.org/N18-2077
%U https://doi.org/10.18653/v1/N18-2077
%P 480-485
Markdown (Informal)
[Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation](https://aclanthology.org/N18-2077) (Apidianaki et al., NAACL 2018)
ACL
- Marianna Apidianaki, Guillaume Wisniewski, Anne Cocos, and Chris Callison-Burch. 2018. Automated Paraphrase Lattice Creation for HyTER Machine Translation Evaluation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 480–485, New Orleans, Louisiana. Association for Computational Linguistics.