Papers by Liwei Wu
Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)
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| Challenge: | Multilingual neural machine translation models suffer from performance degradation when learning multiple languages. |
| Approach: | They propose to use LaSS to jointly train a single unified multilingual MT model. |
| Outcome: | The proposed model gains on 36 language pairs by up to 1.2 BLEU and zero-shot translation with 8.3 BLUE on 30 language pairs. |
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation (2023.findings-acl)
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| Challenge: | Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. |
| Approach: | They propose a framework for multimodal machine translation that utilizes large-scale non-triple data and a multimodal translation dataset. |
| Outcome: | The proposed method can significantly improve translation performance with more non-triple data. |
Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies on multilingual machine translation have ignored the importance of LTs. |
| Approach: | They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions. |
| Outcome: | The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Can Large Language Models Tackle Graph Partitioning? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have remarkable capabilities in understanding complex tasks, but they can only handle graph partitioning tasks that require global perception abilities. |
| Approach: | They propose a pipeline for coarsening, reasoning, and refining to enable LLMs to perform graph partitioning on small-scale graphs. |
| Outcome: | The proposed pipeline can handle graph partitioning tasks on small graphs with coarsening, reasoning, and refining. |
BiasX: “Thinking Slow” in Toxic Content Moderation with Explanations of Implied Social Biases (2023.emnlp-main)
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| Challenge: | Toxicity annotators and content moderators often default to mental shortcuts when making decisions, leading to subtle toxicity being missed and seemingly harmless content being over-detected. |
| Approach: | They propose a framework that provides AI-generated explanations of statements’ implied social biases to enhance content moderation setups. |
| Outcome: | The proposed framework significantly improves content moderation setups by enabling users to think more thoroughly about their decisions. |
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing multilingual machine translation approaches focus on English-centric directions, while non-English directions lag behind. |
| Approach: | They propose a multilingual machine translation system with an emphasis on non-English directions. |
| Outcome: | The proposed model outperforms existing models on English-centric and non-English directions on multilingual translation benchmarks. |