Papers by Mei Tu
ACL 2026 Industry Track: Overview (2026.acl-industry)
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| Challenge: | 153 papers were selected for presentation at the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026). |
| Approach: | the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) organised a dedicated industry track. authors acknowledge the challenges in adapting language technologies for building novel and robust. |
| Outcome: | the industry track attracted 532 papers at the 64th Annual Meeting of the Association for Computational Linguistics . the submissions can be grouped into six different clusters: 1. RAG systems & enterprise knowledge AI; 2. agentic systems and workflows; 3. Language technologies and their applications are an integral and critical part of our daily lives. |
A Lightweight Mixture-of-Experts Neural Machine Translation Model with Stage-wise Training Strategy (2024.findings-naacl)
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| Challenge: | Using mixture-of-experts (MoE) to deal with language heterogeneity is a challenge in neural machine translation (NMT). |
| Approach: | They propose a lightweight MoE-based NMT model that is trained via an elaborate stage-wise training strategy. |
| Outcome: | The proposed model achieves stable improvements in translation tasks by introducing fewer extra parameters compared to baseline models. |
Pretrained Bidirectional Distillation for Machine Translation (2023.acl-long)
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| Challenge: | Existing studies have focused on language knowledge transfer from pretrained models to neural machine translation models. |
| Approach: | They propose to use masked language pretraining to efficiently transfer bidirectional language knowledge to NMT models. |
| Outcome: | The proposed method can significantly improve machine translation performance and achieve competitive or even better results than previous methods. |
CCIM: Cross-modal Cross-lingual Interactive Image Translation (2023.findings-emnlp)
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| Challenge: | Existing research on text image machine translation (TIMT) lacks recognized source language information resulting in a decrease in translation performance. |
| Approach: | They propose a cross-modal cross-lingual interactive model which incorporates source language information by synchronizing source and target language results. |
| Outcome: | The proposed model outperforms end-to-end models and has faster decoding speed with smaller model size than cascade models. |
Long-range Sequence Modeling with Predictable Sparse Attention (2022.acl-long)
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| Challenge: | Existing approaches to capture global context dependencies in sequence modeling suffer from quadratic complexity in time and memory usage. |
| Approach: | They propose an efficient Transformer architecture for fast long-range sequence modeling with a sparse attention matrix and a hidden state cross module. |
| Outcome: | The proposed architecture outperforms the standard multi-head attention and its variants in various long-sequence tasks with low computational costs. |