Papers by Wen Lai
m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter (2022.findings-emnlp)
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| Challenge: | Multilingual neural machine translation models (MNMT) are effective on transferring knowledge between high-resource languages to low-resourced languages. |
| Approach: | They propose a multilingual multi-domain adapter which combines domain and language knowledge using meta-learning with adapters. |
| Outcome: | The proposed model outperforms other adapter methods in a domain shift and language pair translation task. |
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing approaches to multilingual neural machine translation (MNMT) are limited in their ability to handle large amounts of data. |
| Approach: | They propose a framework which only requires target-side monolingual data and a bilingual dictionary to improve the performance of the MNMT model. |
| Outcome: | The proposed framework is more effective than baselines in long-tail and high-resource languages. |
Style-Specific Neurons for Steering LLMs in Text Style Transfer (2024.emnlp-main)
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| Challenge: | Existing LLMs tend to prioritize preserving original meaning over enhancing stylistic differences in TST. |
| Approach: | They propose a novel approach to steering LLMs using style-specific neurons in TST. |
| Outcome: | Empirical results show that the proposed method improves the fluency of the generated text. |
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System (2021.naacl-demos)
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Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, Dídac Surís, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
| Challenge: | We present a new information extraction system that can construct temporal event graphs from news documents. |
| Approach: | They propose a temporal event graph extraction system that can extract news documents . they extend the system from sentence-level event extraction to cross-document cross-media event extraction . |
| Outcome: | The proposed system can extract temporal event graphs from news documents in multiple languages and multiple data modalities. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback (2024.findings-acl)
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| Challenge: | Recent multilingual models support limited number of human languages due to lack of training data for low resource languages. |
| Approach: | They propose a multilingual multilingual LLM that scales to 100 languages . they use a human feedback dataset and a data set to perform multilingual instruction tuning . |
| Outcome: | The proposed model outperforms its peers on five multilingual benchmarks. |
From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora (2025.emnlp-main)
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| Challenge: | Experiments show that models trained on multi-way parallel data outperform those trained on unaligned data. |
| Approach: | They propose a large-scale, high-quality multi-way parallel corpus based on TED Talks that spans 113 languages with up to 50 languages aligned in parallel. |
| Outcome: | The proposed model outperforms models trained on unaligned multilingual data on six multilingual benchmarks. |
Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |