@inproceedings{baskaya-etal-2017-integrating,
title = "Integrating Meaning into Quality Evaluation of Machine Translation",
author = {Ba{\c{s}}kaya, Osman and
Yildiz, Eray and
Tunao{\u{g}}lu, Doruk and
Eren, Mustafa Tolga and
Do{\u{g}}ru{\"o}z, A. Seza},
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1020",
pages = "210--219",
abstract = "Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiments. In the first experiment, the meaning related features are compared to human rankings individually. In the second experiment, combinations of meaning related features and other quality metrics are utilized to predict the same human rankings. The results of our experiments confirm the benefit of these features in predicting human evaluation of translation quality in addition to traditional metrics which focus mainly on form.",
}
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%0 Conference Proceedings
%T Integrating Meaning into Quality Evaluation of Machine Translation
%A Başkaya, Osman
%A Yildiz, Eray
%A Tunaoğlu, Doruk
%A Eren, Mustafa Tolga
%A Doğruöz, A. Seza
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 apr
%I Association for Computational Linguistics
%C Valencia, Spain
%F baskaya-etal-2017-integrating
%X Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiments. In the first experiment, the meaning related features are compared to human rankings individually. In the second experiment, combinations of meaning related features and other quality metrics are utilized to predict the same human rankings. The results of our experiments confirm the benefit of these features in predicting human evaluation of translation quality in addition to traditional metrics which focus mainly on form.
%U https://aclanthology.org/E17-1020
%P 210-219
Markdown (Informal)
[Integrating Meaning into Quality Evaluation of Machine Translation](https://aclanthology.org/E17-1020) (Başkaya et al., EACL 2017)
ACL
- Osman Başkaya, Eray Yildiz, Doruk Tunaoğlu, Mustafa Tolga Eren, and A. Seza Doğruöz. 2017. Integrating Meaning into Quality Evaluation of Machine Translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 210–219, Valencia, Spain. Association for Computational Linguistics.