Just Ask! Evaluating Machine Translation by Asking and Answering Questions

Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, Pavel Pecina


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.
Anthology ID:
2021.wmt-1.58
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Venues:
EMNLP | WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
495–506
Language:
URL:
https://aclanthology.org/2021.wmt-1.58
DOI:
Bibkey:
Cite (ACL):
Mateusz Krubiński, Erfan Ghadery, Marie-Francine Moens, and Pavel Pecina. 2021. Just Ask! Evaluating Machine Translation by Asking and Answering Questions. In Proceedings of the Sixth Conference on Machine Translation, pages 495–506, Online. Association for Computational Linguistics.
Cite (Informal):
Just Ask! Evaluating Machine Translation by Asking and Answering Questions (Krubiński et al., WMT 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wmt-1.58.pdf
Code
 ufal/mteqa
Data
SQuADXQuAD