| Challenge: | Existing methods to incorporate information from other modality, usually static images, are not considered relative to multimodal machine translation. |
| Approach: | They propose a multimodal self-attention method which learns the representation of images based on the text, which avoids encoding irrelevant information in images. |
| Outcome: | The proposed model outperforms previous studies and competitive baselines in terms of various metrics. |
Similar Papers
On Vision Features in Multimodal Machine Translation (2022.acl-long)
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| Challenge: | Recent work on multimodal machine translation (MMT) has focused on the way of incorporating vision features into translation but little attention is given to the quality of vision models. |
| Approach: | They develop a selective attention model to study the patch-level contribution of an image in multimodal machine translation. |
| Outcome: | The proposed model is able to learn translation from the visual modality on probing tasks and is compared with existing models. |
Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation (2021.acl-long)
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| Challenge: | Recent studies report improvements when equipping models with multimodal information, but it remains unclear whether such improvements actually come from the multimodal part. |
| Approach: | They propose to extend conventional text-only translation models with multimodal information by extending them with visual input. |
| Outcome: | The proposed models replicate similar gains as recently developed multimodal-integrated systems achieved, but learn to ignore multimodal information. |
Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective (2022.emnlp-main)
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| Challenge: | Existing studies focus on extracting multi-granularity visual features for integration or designing model architectures for better message passing across various modalities. |
| Approach: | They propose to decompose the informative visual signals into two parts: source-specific information and target-specific info. |
| Outcome: | The proposed method can enhance the visual awareness of MMT models against strong baselines. |
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)
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| Challenge: | Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images. |
| Approach: | They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it. |
| Outcome: | The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available. |
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. |
| Approach: | They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset. |
| Outcome: | The proposed method can significantly improve translation performance with more non-triple data. |
Probing the Need for Visual Context in Multimodal Machine Translation (N19-1)
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| Challenge: | Current work on multimodal machine translation (MMT) suggests that the visual modality is either unnecessary or only marginally beneficial. |
| Approach: | They propose to use the visual modality to combine visual and textual information to generate better translations by partially depriving models from source-side textual context. |
| Outcome: | The proposed model can combine visual and textual information to generate better translations under limited textual context. |
UMTIT: Unifying Recognition, Translation, and Generation for Multimodal Text Image Translation (2024.lrec-main)
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| Challenge: | Current Image machine translation (IMT) relies on a cascaded system that combines Optical Character Recognition (OCR) and a complex process of rendering the translated text back onto the source image. |
| Approach: | They propose a multimodal image-text translation model that generates consistent target images . they use two image-to-text conversion steps to convert images to text to recognize source text . |
| Outcome: | The proposed model outperforms existing methods and surpasses state-of-the-art methods in text recognition tasks. |
Multimodal Machine Translation with Text-Image In-depth Questioning (2025.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) models focus on intermodal interactions, but focus on simple interactions between nouns and entities in image, overlooking global semantic alignment. |
| Approach: | They propose a Text-Image In-depth Questioning method to deepen interactions and optimize translations by utilizing visual data to capture global semantic alignment. |
| Outcome: | The proposed method achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark. |
Supervised Visual Attention for Multimodal Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing studies show that a conventional visual attention mechanism trained in an unsupervised manner is not effective for multimodal neural machine translation. |
| Approach: | They propose a supervised visual attention mechanism for multimodal neural machine translation that captures the relationship between a word and an image region more precisely than a conventional visual attention system. |
| Outcome: | The proposed model improves on English-German and German-English translation tasks and English-Japanese and Japanese-English tasks using the Flickr30k Entities JP dataset. |
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs (2023.findings-emnlp)
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| Challenge: | Existing studies show that multimodal machine translation systems exhibit decreased sensitivity to visual information when text inputs are complete. |
| Approach: | They propose to generate parallel VQA style pairs from source text to foster more robust cross-modal interaction. |
| Outcome: | The proposed approach generates parallel VQA style pairs from the source text, fostering more robust cross-modal interaction. |