Papers with multilingual translation models
CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units (2024.acl-srw)
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| Challenge: | Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. |
| Approach: | They synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a language-to-text translation dataset, CoVoST 2. |
| Outcome: | The proposed model outperforms two monolingual models and is better at code-switching translation into English than non-English. |
Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models (2026.eacl-long)
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| Challenge: | Existing evidence on the intrinsic difficulty of multilingual modeling is limited to small monolingual models or bilingual models trained from scratch. |
| Approach: | They propose to use typological properties to determine the difficulty of modeling a language . they analyze two large pre-trained multilingual translation models . |
| Outcome: | The proposed models are based on two large pre-trained models of encoder-decoder and decoder-only machine translation. |
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. |
Don’t Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation (2024.acl-long)
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| Challenge: | Neural machine translation models estimate probabilities of target sentences given source sentences, but these estimates may not align with human judgments. |
| Approach: | They propose a method that synthesizes translations using a quality estimation metric . they compare it with beam search and recent reranking techniques . |
| Outcome: | The proposed method outperforms other methods in large language models and multilingual translation models. |
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)
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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging. |
| Approach: | They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer . |
| Outcome: | The proposed model performs on par with or better than substantially larger baselines. |