Papers with multilingual machine translation
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice? (2024.emnlp-main)
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| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
| Approach: | They revisit the importance of large language models for translation by fine-tuning on 32 parallel sentences. |
| Outcome: | The proposed model can be fine-tuned on as few as 32 parallel sentences . however, the choice of direction is critical to avoid misinterpretation, the authors say . |
Multilingual, Multi-scale and Multi-layer Visualization of Intermediate Representations (D19-3)
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| Challenge: | Currently, the main alternatives to deal with sequences are Recurrent Neural Networks (RNN) architectures and the Transformer. |
| Approach: | They propose a web-based tool that visualizes the sentence and token representations of RNNs and Transformer architectures at the sentence level. |
| Outcome: | The proposed visualization tool analyses gender inequalities in contextual word embeddings and the common language representation in a multilingual machine translation system. |
SkipCLM: Enhancing Crosslingual Alignment of Decoder Transformer Models via Contrastive Learning and Skip Connection (2025.naacl-srw)
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| Challenge: | Existing contrastive learning methods for cross-lingual alignment are not effective for multilingual machine translation tasks. |
| Approach: | They propose a method that augments contrastive learning for cross-lingual alignment with a trainable skip connection to preserve information crucial for accurate target language generation. |
| Outcome: | Experiments with XGLM-564M on the Flores-101 benchmark show that the proposed method preserves crucial information crucial for accurate target language generation. |
No Error Left Behind: Multilingual Grammatical Error Correction with Pre-trained Translation Models (2024.eacl-long)
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| Challenge: | Grammatical Error Correction (GEC) research has primarily focused on English with little coverage for other languages. |
| Approach: | They propose a multilingual machine translation model that can be fine-tuned to improve error correction out-of-the-box. |
| Outcome: | The proposed model outperforms similar-sized MT5 models and competes favourably with larger models. |
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders (2021.eacl-main)
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
| Approach: | They propose an encoder-decoder approach that can be extended to new languages by learning their corresponding modules. |
| Outcome: | The proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average while allowing to add new languages without retraining the rest of the modules. |
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution (2021.naacl-main)
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| Challenge: | Existing approaches to multilingual machine translation rely on training models on monolingual data for all languages in a multitask setup. |
| Approach: | They propose a vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models by combining monolingual data with a dictionary. |
| Outcome: | The proposed model improves on existing models by preserving the original model and allowing for competitive performance even with only monolingual data. |
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages (2023.eacl-main)
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| Challenge: | Existing multilingual machine translation models need to be upgraded as data becomes available in more languages. |
| Approach: | They propose three techniques that speed up the effective learning of new languages and alleviate catastrophic forgetting . |
| Outcome: | The proposed techniques exceed the performance of a same-sized baseline model with 30% computation and recover the performance a larger model trained from scratch with over 50% reduction in computation. |
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. |
AfriMMT-EA: Multi-domain Machine Translation for Low-Resource East African Languages (2026.findings-eacl)
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Naome A Etori, Kelechi Ezema, Nathaniel Romney Robinson, Davis David, Alfred Malengo Kondoro, Elisha Ondieki Makori, Michael Samwel Mollel, Maria Gini
| Challenge: | Recent advances in open-source large language models have demonstrated strong multilingual capabilities through data-efficient adaptation strategies. |
| Approach: | They propose to use AfriMMT-EA to refine two multilingual versions of Gemma-3 to better understand the region's linguistic and cultural diversity. |
| Outcome: | The proposed datasets comprise 54 local languages across five East African countries. |
Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations (2020.emnlp-main)
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| Challenge: | Recent studies consider linguistic typology as a potential source of knowledge to support multilingual natural language processing (NLP) tasks. |
| Approach: | They propose to fuse both views using canonical correlation analysis and use it to infer typological features and language phylogenies to construct a multi-view language vector space for multilingual machine translation. |
| Outcome: | The proposed model achieves competitive translation accuracy in multilingual machine translation tasks without expensive retraining of massive multilingual or ranking models. |
Latent Group Dropout for Multilingual and Multidomain Machine Translation (2022.findings-naacl)
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| Challenge: | Multidomain and multilingual machine translation often rely on parameter sharing strategies, which are hardcoded in the network architecture, independent of the similarities between tasks. |
| Approach: | They propose a method to take advantage of similarities by using a latent-variable model and develop techniques to train this model end-to-end. |
| Outcome: | The proposed model improves translation performance without increasing the model size. |
Competence-based Curriculum Learning for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing multilingual machine translation models face an imbalance problem due to the different learning competencies of different languages. |
| Approach: | They propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M, to help schedule the high resource languages and low resource languages. |
| Outcome: | The proposed approach achieves a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset. |
Counter-Interference Adapter for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing approaches to multilingual machine translation suffer from performance degradation, resulting in a single model being inferior to separately trained bilingual models on resource-rich languages. |
| Approach: | They propose a transformer-based model with a small parameter overhead for multilingual machine translation that outperforms strong multilingual baselines on 64 of 66 language directions. |
| Outcome: | The proposed model outperforms strong multilingual baselines on 64 of 66 language directions, 42 of which have above 0.5 BLEU improvement. |
Tricks for Training Sparse Translation Models (2022.naacl-main)
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| Challenge: | Multitask learning with an unbalanced data distribution skews model learning towards high resource tasks. |
| Approach: | They propose to use a temperature heating mechanism and dense pre-training to mitigate this by training models with a fixed model capacity. |
| Outcome: | The proposed techniques improve performance on two multilingual translation benchmarks compared to BASELayers and Dense scaling baselines and in combination, more than 2x model convergence speed. |
Backdoor Attacks on Multilingual Machine Translation (2024.naacl-long)
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| Challenge: | Recent studies have shown that multilingual machine translation systems are vulnerable to backdoor attacks through data poisoning. |
| Approach: | They propose to investigate the security of multilingual machine translation systems by exposing poisoned data into low-resource languages to cause malicious translations. |
| Outcome: | The proposed method achieves an average of 20% success rate in attacking high-resource languages. |
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have shown continuously improving multilingual capabilities. |
| Approach: | They evaluate the ability of open LLMs to handle multilingual machine translation tasks using a parallel-first monolingual-second data mixing strategy. |
| Outcome: | The proposed model outperforms state-of-the-art models and achieves competitive performance with Google Translate and GPT-4-turbo. |
Multilingual Translation from Denoising Pre-Training (2021.findings-acl)
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Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan
| Challenge: | Recent work shows potential of training one model for multilingual machine translation . but little has been explored on the potential to combine denoising pretraining with multilingual translation in a single model. |
| Approach: | They propose to combine denoising pretraining with multilingual machine translation in a single model. |
| Outcome: | The proposed model improves over models trained from scratch and bilingually for translation into English. |
Code-Switched Text Synthesis in Unseen Language Pairs (2023.findings-acl)
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| Challenge: | Existing efforts on text synthesis for code-switching require training on code-witched texts in the target language pairs. |
| Approach: | They propose a model that synthesizes code-switched texts for language pairs absent from training data by adding an additional code-sharing module to a pre-trained machine translation model. |
| Outcome: | The proposed model synthesizes code-switched texts for language pairs lacking from training data. |
Alternative Input Signals Ease Transfer in Multilingual Machine Translation (2022.acl-long)
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| Challenge: | Recent work in multilingual machine translation (MMT) has focused on the potential of positive transfer between languages. |
| Approach: | They propose to augment training data with alternative signals that unify different writing systems, such as phonetic, romanized, and transliterated input. |
| Outcome: | The proposed model outperforms strong ensemble baselines on Indic and Turkic languages by 1.3 BLEU points on both languages. |
Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America (2022.coling-1)
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| Challenge: | Existing studies show that multi-parallel translation models can overfit when training data are limited. |
| Approach: | They introduce a regularizer which penalizes translation models when they represent source sentences with identical target translations in divergent ways. |
| Outcome: | The proposed model improves when the target data for all language pairs are identical. |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
StableMoE: Stable Routing Strategy for Mixture of Experts (2022.acl-long)
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| Challenge: | Existing learning-to-route methods suffer from the routing fluctuation issue . with the model scale growing, training speed will go slower and memory requirements are heavy . |
| Approach: | They propose a Mixture-of-Experts technique that can scale up the model size of Transformers with an affordable computational overhead. |
| Outcome: | The proposed method outperforms existing learning-to-route methods on language modeling and multilingual machine translation. |
Multilingual Parallel Corpus for Global Communication Plan (L18-1)
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| Challenge: | In this paper, we introduce the Global Communication Plan (GCP) Corpus . the corpus is sentence-aligned and covers ten languages, including many Asian languages . |
| Approach: | They introduce the Global Communication Plan (GCP) Corpus, a multilingual parallel corpus . it is sentence-aligned and covers ten languages, including many Asian languages . |
| Outcome: | The proposed corpus is sentence-aligned and covers ten languages, including many Asian languages. |
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)
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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran
| Challenge: | In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low. |
| Approach: | They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
| Outcome: | The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
On the Off-Target Problem of Zero-Shot Multilingual Neural Machine Translation (2023.findings-acl)
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| Challenge: | Despite its success, multilingual neural machine translation suffers from the off-target issue, where the translation is in the wrong language. |
| Approach: | They propose a language-aware vocabulary sharing algorithm that can be used to increase the lexical distance between languages by isolating the vocab of different languages in the decoder. |
| Outcome: | The proposed algorithm reduces off-target rate for 90 translation tasks from 29% to 8%, while improving overall BLEU score by an average of 1.9 points without extra training cost or sacrificing the supervised directions’ performance. |
Balancing Training for Multilingual Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing methods to train multilingual machine translation models are imbalanced and heterogeneous data is wildly varying. |
| Approach: | They propose a method that automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages. |
| Outcome: | The proposed method outperforms baselines on two sets of languages under one-to-many and many-to-1 MT settings and offers flexible control over which languages are optimized. |
MLAS-LoRA: Language-Aware Parameters Detection and LoRA-Based Knowledge Transfer for Multilingual Machine Translation (2025.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance even with limited parallel data. |
| Approach: | They propose a multiple language-aware LoRA knowledge transfer framework that selectively adapts LLMs to MT by transferring knowledge from a large teacher to a small student model. |
| Outcome: | The proposed framework outperforms baseline models on multilingual language pairs by +1.7 BLEU on average. |
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model (2024.findings-acl)
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| Challenge: | Large language models lack explicit alignment between source and target contexts, leading to unfaithful translations. |
| Approach: | They propose three learning strategies to encourage LLMs to pay more attention to source context . they use a dataset to test the effectiveness of their model across multiple language pairs . |
| Outcome: | The proposed model reduces hallucinatory translation and improves fidelity across multiple languages. |
Causes and Cures for Interference in Multilingual Translation (2023.acl-long)
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| Challenge: | Existing methods to reduce interference in multilingual machine translation are often computationally intensive and do not always work. |
| Approach: | They propose to reduce interference in multilingual machine translation models by enlarging the model and tuning the sampling temperature to control the proportion of each language pair in the data. |
| Outcome: | The proposed model size, data size, and proportion of each language pair within the dataset determine interference (or synergy) . |
Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Sparsely gated Mixture of Experts (MoE) models are a compute-efficient method to scale model capacity for multilingual machine translation tasks. |
| Approach: | They propose a regularization strategy that prevents over-fitting of MoE models on low-resource tasks and conditional MoE Routing and curriculum learning methods that prevent over- fitting. |
| Outcome: | The proposed methods improve the performance of MoE models on low-resource tasks without adversely affecting high-res tasks. |
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation (2025.findings-acl)
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| Challenge: | Existing work in LLM-based MMT typically mitigates the Curse of Multilinguality . asymmetric phenomenon in linguistic conflicts and synergy varies in different translation directions . |
| Approach: | They propose a direction-aware training approach to address asymmetry in linguistic conflicts and synergy . they propose X-ALMA-13B-Pretrain with multilingual pre-training to achieve comparable performance . |
| Outcome: | The proposed method achieves comparable performance to X-ALMA-13B-Pretrain (only SFT) with fewer pretraining tokens and 17B parameters. |