Generative Imagination Elevates Machine Translation

Quanyu Long, Mingxuan Wang, Lei Li


Abstract
There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existing multimodal neural machine translation methods (MNMT) require triplets of bilingual sentence - image for training and tuples of source sentence - image for inference. In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate visual representation from the source sentence, and then utilizes both source sentence and the “imagined representation” to produce a target translation. Unlike previous methods, it only needs the source sentence at the inference time. Experiments demonstrate that ImagiT benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines. Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.
Anthology ID:
2021.naacl-main.457
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5738–5748
Language:
URL:
https://aclanthology.org/2021.naacl-main.457
DOI:
10.18653/v1/2021.naacl-main.457
Bibkey:
Cite (ACL):
Quanyu Long, Mingxuan Wang, and Lei Li. 2021. Generative Imagination Elevates Machine Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5738–5748, Online. Association for Computational Linguistics.
Cite (Informal):
Generative Imagination Elevates Machine Translation (Long et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.457.pdf
Video:
 https://aclanthology.org/2021.naacl-main.457.mp4
Data
Flickr30kMS COCOMulti30K