Adversarial Evaluation of Multimodal Machine Translation
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Adversarial Evaluation of Multimodal Machine Translation. / Elliott, Desmond.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2018. s. 2974-2978.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Adversarial Evaluation of Multimodal Machine Translation
AU - Elliott, Desmond
PY - 2018
Y1 - 2018
N2 - The promise of combining vision and language in multimodal machine translation is that systems will produce better translations by leveraging the image data. However, inconsistent results have lead to uncertainty about whether the images actually improve translation quality. We present an adversarial evaluation method to directly examine the utility of the image data in this task. Our evaluation measures whether multimodal translation systems perform better given either the congruentimage or a random incongruent image, in add ition to the correct source language sentence. We find that two out of three publicly available systems are sensitive to this perturbation of the data, and recommend that all systems pass this evaluation in the future
AB - The promise of combining vision and language in multimodal machine translation is that systems will produce better translations by leveraging the image data. However, inconsistent results have lead to uncertainty about whether the images actually improve translation quality. We present an adversarial evaluation method to directly examine the utility of the image data in this task. Our evaluation measures whether multimodal translation systems perform better given either the congruentimage or a random incongruent image, in add ition to the correct source language sentence. We find that two out of three publicly available systems are sensitive to this perturbation of the data, and recommend that all systems pass this evaluation in the future
M3 - Article in proceedings
SN - 978-1-948087-84-1
SP - 2974
EP - 2978
BT - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics
Y2 - 31 October 2018 through 4 November 2018
ER -
ID: 230797240