Towards Multimodal Simultaneous Neural Machine Translation

• URL: http://arxiv.org/abs/2004.03180v2
• Date: Fri, 23 Oct 2020 04:38:38 GMT
• Title: Towards Multimodal Simultaneous Neural Machine Translation
• Authors: Aizhan Imankulova, Masahiro Kaneko, Tosho Hirasawa and Mamoru Komachi
• Abstract summary: Simultaneous translation involves translating a sentence before the speaker's utterance is completed in order to realize real-time understanding. This task is significantly more challenging than the general full sentence translation because of the shortage of input information during decoding. We propose multimodal simultaneous neural machine translation (MSNMT), which leverages visual information as an additional modality.
• Score: 28.536262015508722
• License: http://creativecommons.org/licenses/by/4.0/
• 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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