| 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. |
| Approach: | They propose to use a multilingual multi-modal encoder to bootstrap mTTI systems that can be translated into other languages. |
| Outcome: | The proposed approach mitigates the language gap and improves on standard mTTI datasets. |
Similar Papers
English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning (D19-52)
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| Challenge: | Multi-modal translation is an emerging task of the MT community, where visual features of image combine with textual features of parallel source-target text to translate sentences. |
| Approach: | They propose to use convolutional neural net-works and visual geometry to extract image features and attention-based Neural MachineTranslation (NMT) system for translation. |
| Outcome: | The proposed multi-modal translation system improves translation quality and improves the quality of the captions of the images. |
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. |
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2021.naacl-main)
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Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel
| Challenge: | Current natural language processing pipelines often use transfer learning, where a model is pre-trained on a data-rich task before being fine-tuned on . this significantly limits their use given that roughly 80% of the world population does not speak English. |
| Approach: | They introduce a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. |
| Outcome: | The proposed model achieves state-of-the-art on multilingual benchmarks and a simple technique to prevent accidental translation in the zero-shot setting. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
| Approach: | They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders. |
| Outcome: | The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets. |
Cross-lingual Cross-modal Pretraining for Multimodal Retrieval (2021.naacl-main)
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| Challenge: | Recent pretrained vision-language models have achieved impressive performance on cross-modal retrieval tasks in English. |
| Approach: | They propose a new approach to learn cross-lingual cross-modal representations for matching images and captions in multiple languages using an annotated corpus. |
| Outcome: | The proposed model achieves impressive performance on two multimodal multilingual image caption benchmarks: Multi30k with German captions and MSCOCO with Japanese captions. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
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| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021.acl-short)
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| Challenge: | Recent studies have shown that cross-lingual transfer learning in pre-trained multilingual models could be improved further by incorporating parallel data. |
| Approach: | They propose to integrate parallel data into mT5 pre-training to improve results on downstream multilingual and cross-lingual tasks. |
| Outcome: | The proposed model improves cross-lingual transfer significantly in small fine-tuning datasets and small model sizes. |
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
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| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
| Outcome: | The proposed approach narrows the performance gap with bilingual models and improves zero-shot performance by 10 BLEU, approaching conventional pivot-based methods. |