Papers with low-resource translation
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. |
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation (2022.lrec-1)
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| Challenge: | Transfer learning is a promising direction for low-resource neural machine translation (NMT) but it introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. |
| Approach: | They conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. |
| Outcome: | The results suggest that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural machine translation. |
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. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to train NMT models rely on sparse parallel data . a variety of PC variants yield significant improvements for low-resource NMT . |
| Approach: | They propose to transfer well-trained NMT models to low-resource languages by bidirectionally-adaptive learning strategy . they divide inner constituents of Parent encoder into two "teams" aiming to adapt to characteristics of low- and high-resourced languages . |
| Outcome: | The proposed method improves on low-resource NMT models with a variety of PC variants. |
Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation (2026.findings-acl)
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| Challenge: | Existing methods for training large language models rely heavily on high-quality parallel data, which are often scarce or unavailable for low-resource languages. |
| Approach: | They propose a reinforcement training method using only monolingual text to elevate LLMs’ translation capabilities on massive low-resource languages while retaining their performance on high-resourced languages. |
| Outcome: | The proposed model outperforms LLaMAX, one of the strongest open-source multilingual LLMs on 1,414 language directions on Flores-101 dataset. |