On-the-Fly Fusion of Large Language Models and Machine Translation (2024.findings-naacl)
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| Challenge: | a weaker-at-translation LLM can improve translations of a NMT model, compared to a strong dedicated model. |
| Approach: | They propose to ensemble a neural machine translation model with a large language model, prompted on the same task and input. |
| Outcome: | The proposed method can be combined with various techniques from LLM prompting, such as in context learning and translation context. |
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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. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
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Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
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Massively Multilingual Neural Machine Translation (N19-1)
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| Challenge: | Multilingual Neural Machine Translation models support translation from multiple source languages into multiple target languages. |
| Approach: | They perform extensive experiments in training massively multilingual NMT models involving up to 103 distinct languages and 204 translation directions simultaneously. |
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Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
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| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
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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. |
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Towards Demonstration-Aware Large Language Models for Machine Translation (2024.findings-acl)
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| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
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Improving Language Model Integration for Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing methods to integrate external language models into machine translation systems have been based on the assumption that the external model learns an implicit target-side language model at decoding time. |
| Approach: | They transfer this concept to the task of machine translation and compare it with the most prominent way of including additional monolingual data - namely back-translation. |
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Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
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Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich Languages (L18-1)
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| Challenge: | Using multilingual and multi-way neural machine translation approaches is a major advantage . training NMT systems for individual language pairs takes significantly more time than training of SMT systems . |
| Approach: | They propose to employ multilingual and multi-way neural machine translation approaches for morphologically rich languages such as Estonian and Russian. |
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