Papers by Mengyu Bu
Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
| Approach: | They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders. |
| Outcome: | The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets. |
AlignX: Advancing Multilingual Large Language Models with Multilingual Representation Alignment (2025.emnlp-main)
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| Challenge: | Multilingual large language models (LLMs) possess impressive multilingual understanding and generation capabilities, but performance and cross-lingual alignment often lag for non-dominant languages. |
| Approach: | They propose a representation-level framework to enhance multilingual performance of pre-trained LLMs by integrating multilingual semantic alignment and language feature integration. |
| Outcome: | The proposed framework improves multilingual capability of pre-trained LLMs by bringing representations closer and improving cross-lingual alignment. |
MoCE: Adaptive Mixture of Contextualization Experts for Byte-based Neural Machine Translation (2025.naacl-long)
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| Challenge: | Byte-based machine translation systems can be used in multilingual settings. |
| Approach: | They propose a method that maps each character to specific byte(s) they propose byte-level tokenization that eliminates unknown words . |
| Outcome: | The proposed method outperforms existing methods without manual adjustment of hyper-parameters and surpasses subword-based models with fewer parameters in Ted-59 dataset. |
Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality (2026.acl-long)
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| Challenge: | Large language models exhibit strong general intelligence, yet their multilingual performance remains imbalanced. |
| Approach: | They propose a compositional encoder-LLM-decoder architecture that offloads multilingual understanding to external pretrained translation models while preserving the LLM as an English-centric core for general knowledge processing and reasoning. |
| Outcome: | The proposed architecture outperforms baseline models on four large language models across understanding, reasoning, and generation. |