Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles

Iacer Calixto, Daniel Stein, Evgeny Matusov, Sheila Castilho, Andy Way

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Abstract
In this paper, we study how humans perceive the use of images as an additional knowledge source to machine-translate user-generated product listings in an e-commerce company. We conduct a human evaluation where we assess how a multi-modal neural machine translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attention-based NMT and a phrase-based statistical machine translation (PBSMT) model. We evaluate translations obtained with different systems and also discuss the data set of user-generated product listings, which in our case comprises both product listings and associated images. We found that humans preferred translations obtained with a PBSMT system to both text-only and multi-modal NMT over 56% of the time. Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88% of the time, which suggests that images do help NMT in this use-case.
Anthology ID:
W17-2004
Volume:
Proceedings of the Sixth Workshop on Vision and Language
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Anya Belz, Erkut Erdem, Katerina Pastra, Krystian Mikolajczyk
Venue:
VL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
31–37
Language:
URL:
https://aclanthology.org/W17-2004
DOI:
10.18653/v1/W17-2004
Bibkey:
Cite (ACL):
Iacer Calixto, Daniel Stein, Evgeny Matusov, Sheila Castilho, and Andy Way. 2017. Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles. In Proceedings of the Sixth Workshop on Vision and Language, pages 31–37, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles (Calixto et al., VL 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-2004.pdf