@inproceedings{krubinski-etal-2021-just,
title = "Just Ask! Evaluating Machine Translation by Asking and Answering Questions",
author = "Krubi{\'n}ski, Mateusz and
Ghadery, Erfan and
Moens, Marie-Francine and
Pecina, Pavel",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wmt-1.58",
pages = "495--506",
abstract = "In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems. Building on recent work on the evaluation of abstractive text summarization, we propose a new metric for system-level MT evaluation, compare it with other state-of-the-art solutions, and show its robustness by conducting experiments for various MT directions.",
}
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<abstract>In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems. Building on recent work on the evaluation of abstractive text summarization, we propose a new metric for system-level MT evaluation, compare it with other state-of-the-art solutions, and show its robustness by conducting experiments for various MT directions.</abstract>
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%0 Conference Proceedings
%T Just Ask! Evaluating Machine Translation by Asking and Answering Questions
%A Krubiński, Mateusz
%A Ghadery, Erfan
%A Moens, Marie-Francine
%A Pecina, Pavel
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F krubinski-etal-2021-just
%X In this paper, we show that automatically-generated questions and answers can be used to evaluate the quality of Machine Translation (MT) systems. Building on recent work on the evaluation of abstractive text summarization, we propose a new metric for system-level MT evaluation, compare it with other state-of-the-art solutions, and show its robustness by conducting experiments for various MT directions.
%U https://aclanthology.org/2021.wmt-1.58
%P 495-506
Markdown (Informal)
[Just Ask! Evaluating Machine Translation by Asking and Answering Questions](https://aclanthology.org/2021.wmt-1.58) (Krubiński et al., WMT 2021)
ACL