Papers by Chengpeng Fu
Gradient Consistency-based Parameter Allocation for Multilingual Neural Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation models are often prone to parameter interference . a common problem is that the model compromises with the language diversity to find a solution . |
| Approach: | They propose a method that allocates parameters based on consistency between the gradients of the individual language and the average gradient. |
| Outcome: | The proposed method reduces parameter interference and improves translation quality. |
Aligning Translation-Specific Understanding to General Understanding in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language models (LLMs) have remarkable abilities in understanding complex texts . however, understanding misalignment leads to LLMs mistakenly translating complex concepts . |
| Approach: | They propose a translation process that aligns the translation-specific understanding with the general understanding to improve translation quality and reduce translation literalness. |
| Outcome: | The proposed translation process improves translation quality and reduces translation literalness by -25% -51%. |
Breaking Language Preference in Multilingual RAG via Language-Controllable Retrieval and Language-Agnostic Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to improve retrieval accuracy and generation quality of large language models suffer from language preference. |
| Approach: | They propose a framework that explicitly disentangles multilingual RAG into language-controllable retrieval and language-agnostic reasoning. |
| Outcome: | Experimental results show that the proposed approach outperforms baselines across multilingual benchmarks. |
Enabling Unsupervised Neural Machine Translation with Word-level Visual Representations (2023.findings-emnlp)
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| Challenge: | Unsupervised neural machine translation methods have been observed to make particular errors in comparison to supervised machine translation, such as confusing nouns that pertain to the same semantic category. |
| Approach: | They propose a method that incorporates images at the word level to augment lexical mappings. |
| Outcome: | Experiments on a multi-lingual dataset show that the proposed method generates more accurate translations with only monolingual data. |