Papers by Xiaolin Wang
M3: A Multi-View Fusion and Multi-Decoding Network for Multi-Document Reading Comprehension (2022.emnlp-main)
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| Challenge: | Existing methods for multi-document reading comprehension cannot make full of the advantages of both approaches. |
| Approach: | They propose a multi-view fusion and multi-decoding method that integrates multiple documents for answering questions. |
| Outcome: | The proposed method improves on two mainstream multi-document reading comprehension datasets. |
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task (2022.coling-1)
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Liang Wen, Juan Li, Houfeng Wang, Yingwei Luo, Xiaolin Wang, Xiaodong Zhang, Zhicong Cheng, Dawei Yin
| Challenge: | Existing answer selection approaches for community question answering lack additional answer summaries due to redundancy and lengthiness issues of crowdsourced answers. |
| Approach: | They constructed a dataset which contains a corresponding reference summary for each original lengthy answer. |
| Outcome: | The proposed model improves the performance of a question and candidate answer on a WikiHowQA dataset. |
CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++ (D18-2)
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| Challenge: | Neural machine translation (NMT) has made remarkable progress over the past few years. |
| Approach: | They propose to use C++ and NVIDIA’s GPU-accelerated libraries to build an open-source neural machine translation toolkit called CytonMT. |
| Outcome: | The proposed toolkit accelerates the training speed by 64.5% to 110.8% on neural networks of various sizes, and achieves competitive translation quality. |
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning (2025.coling-main)
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| Challenge: | Low-rank adaptation and its variants have been popular due to their ability to avoid excessive inference costs. |
| Approach: | They propose a low-rank adaptation method that enables high-rank updates with low costs while leveraging semantic and linguistic information inherent in pre-trained weight. |
| Outcome: | The proposed method outperforms LoRA and other fine-tuning methods across tasks with less trainable parameters. |
Japanese-to-English Simultaneous Dubbing Prototype (2023.acl-demo)
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| Challenge: | Our system translates and replaces the original speech of a live video stream in a simultaneous manner. |
| Approach: | They propose a simultaneous dubbing prototype that translates and replaces the original speech of a live video stream in a simultaneous manner. |
| Outcome: | The proposed system achieves a low average latency of 11.90 seconds and meets a smoothness criterion. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |