PEIT: Bridging the Modality Gap with Pre-trained Models for End-to-End Image Translation (2023.acl-long)
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| Challenge: | Image translation is a task that translates an image containing text in the source language to the target language. |
| Approach: | They propose an end-to-end image translation framework that bridges the modality gap between visual inputs and textual inputs/outputs of machine translation (MT). |
| Outcome: | The proposed framework outperforms existing models on a large-scale image translation corpus . it significantly outperformed both cascaded and strong models on the e-commerce domain . |
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| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
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Understanding and Bridging the Modality Gap for Speech Translation (2023.acl-long)
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| Challenge: | Existing methods to improve end-to-end speech translation (ST) use multitask learning, but there is always a modality gap between ST and MT due to the differences between speech and text. |
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In-Image Neural Machine Translation with Segmented Pixel Sequence-to-Sequence Model (2023.findings-emnlp)
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| Challenge: | In-Image Machine Translation (IIMT) aims to convert images containing texts from one language to another. |
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| Challenge: | Multi-modal machine translation (MMT) aimed at using images to help disambiguate the target during translation but recent studies showed that visual features are either negligible or incremental. |
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MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)
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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu
| Challenge: | Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge . |
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RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training (2023.findings-acl)
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| Challenge: | Existing V&L pre-training methods rely on strictly-aligned multilingual image-text pairs generated from English-centric datasets. |
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CCIM: Cross-modal Cross-lingual Interactive Image Translation (2023.findings-emnlp)
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| Challenge: | Existing research on text image machine translation (TIMT) lacks recognized source language information resulting in a decrease in translation performance. |
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Translation-Enhanced Multilingual Text-to-Image Generation (2023.acl-long)
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| Challenge: | Existing models for text-to-image generation are mostly based on the English language due to the lack of annotated image-caption data in other languages. |
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Towards Zero-shot Learning for End-to-end Cross-modal Translation Models (2023.findings-emnlp)
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| Challenge: | End-to-end zero-shot speech translation model is based on a zero-shot approach, but it is less competitive because of the limited amount of data available for multiple modalities. |
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Pushing the Limits of Zero-shot End-to-End Speech Translation (2024.findings-acl)
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| Challenge: | Existing approaches to end-to-end Speech Translation (ST) systems require limited data, which can cause data scarcity and performance degradation. |
| Approach: | They propose a method for zero-shot ST that bridges the modality gap without any paired ST data. |
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