Towards Multimodal Simultaneous Neural Machine Translation
Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa, Mamoru Komachi
Abstract
Simultaneous translation involves translating a sentence before the speaker’s utterance is completed in order to realize real-time understanding in multiple languages. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. To alleviate this shortage, we propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality. Our experiments with the Multi30k dataset showed that MSNMT significantly outperforms its text-only counterpart in more timely translation situations with low latency. Furthermore, we verified the importance of visual information during decoding by performing an adversarial evaluation of MSNMT, where we studied how models behaved with incongruent input modality and analyzed the effect of different word order between source and target languages.- Anthology ID:
- 2020.wmt-1.70
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 594–603
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.70
- DOI:
- Bibkey:
- Cite (ACL):
- Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa, and Mamoru Komachi. 2020. Towards Multimodal Simultaneous Neural Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 594–603, Online. Association for Computational Linguistics.
- Cite (Informal):
- Towards Multimodal Simultaneous Neural Machine Translation (Imankulova et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.70.pdf
- Video:
- https://slideslive.com/38939559
- Code
- toshohirasawa/mst
Export citation
@inproceedings{imankulova-etal-2020-towards,
title = "Towards Multimodal Simultaneous Neural Machine Translation",
author = "Imankulova, Aizhan and
Kaneko, Masahiro and
Hirasawa, Tosho and
Komachi, Mamoru",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.70",
pages = "594--603",
abstract = "Simultaneous translation involves translating a sentence before the speaker{'}s utterance is completed in order to realize real-time understanding in multiple languages. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. To alleviate this shortage, we propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality. Our experiments with the Multi30k dataset showed that MSNMT significantly outperforms its text-only counterpart in more timely translation situations with low latency. Furthermore, we verified the importance of visual information during decoding by performing an adversarial evaluation of MSNMT, where we studied how models behaved with incongruent input modality and analyzed the effect of different word order between source and target languages.",
}
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%0 Conference Proceedings %T Towards Multimodal Simultaneous Neural Machine Translation %A Imankulova, Aizhan %A Kaneko, Masahiro %A Hirasawa, Tosho %A Komachi, Mamoru %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F imankulova-etal-2020-towards %X Simultaneous translation involves translating a sentence before the speaker’s utterance is completed in order to realize real-time understanding in multiple languages. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. To alleviate this shortage, we propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality. Our experiments with the Multi30k dataset showed that MSNMT significantly outperforms its text-only counterpart in more timely translation situations with low latency. Furthermore, we verified the importance of visual information during decoding by performing an adversarial evaluation of MSNMT, where we studied how models behaved with incongruent input modality and analyzed the effect of different word order between source and target languages. %U https://aclanthology.org/2020.wmt-1.70 %P 594-603
Markdown (Informal)
[Towards Multimodal Simultaneous Neural Machine Translation](https://aclanthology.org/2020.wmt-1.70) (Imankulova et al., WMT 2020)
- Towards Multimodal Simultaneous Neural Machine Translation (Imankulova et al., WMT 2020)
ACL
- Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa, and Mamoru Komachi. 2020. Towards Multimodal Simultaneous Neural Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 594–603, Online. Association for Computational Linguistics.