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    <abstract>Text image machine translation (TIMT) which translates source language text images into target language texts has attracted intensive attention in recent years. Although the end-to-end TIMT model directly generates target translation from encoded text image features with an efficient architecture, it lacks the recognized source language information resulting in a decrease in translation performance. In this paper, we propose a novel Cross-modal Cross-lingual Interactive Model (CCIM) to incorporate source language information by synchronously generating source language and target language results through an interactive attention mechanism between two language decoders. Extensive experimental results have shown the interactive decoder significantly outperforms end-to-end TIMT models and has faster decoding speed with smaller model size than cascade models.</abstract>
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%0 Conference Proceedings
%T CCIM: Cross-modal Cross-lingual Interactive Image Translation
%A Ma, Cong
%A Zhang, Yaping
%A Tu, Mei
%A Zhao, Yang
%A Zhou, Yu
%A Zong, Chengqing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ma-etal-2023-ccim
%X Text image machine translation (TIMT) which translates source language text images into target language texts has attracted intensive attention in recent years. Although the end-to-end TIMT model directly generates target translation from encoded text image features with an efficient architecture, it lacks the recognized source language information resulting in a decrease in translation performance. In this paper, we propose a novel Cross-modal Cross-lingual Interactive Model (CCIM) to incorporate source language information by synchronously generating source language and target language results through an interactive attention mechanism between two language decoders. Extensive experimental results have shown the interactive decoder significantly outperforms end-to-end TIMT models and has faster decoding speed with smaller model size than cascade models.
%R 10.18653/v1/2023.findings-emnlp.330
%U https://aclanthology.org/2023.findings-emnlp.330
%U https://doi.org/10.18653/v1/2023.findings-emnlp.330
%P 4959-4965

Markdown (Informal)

[CCIM: Cross-modal Cross-lingual Interactive Image Translation](https://aclanthology.org/2023.findings-emnlp.330) (Ma et al., Findings 2023)

• CCIM: Cross-modal Cross-lingual Interactive Image Translation (Ma et al., Findings 2023)

ACL

• Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao, Yu Zhou, and Chengqing Zong. 2023. CCIM: Cross-modal Cross-lingual Interactive Image Translation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4959–4965, Singapore. Association for Computational Linguistics.