Papers by Xuemin Liu
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection (2022.findings-emnlp)
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Haoran Meng, Zheng Xin, Tianyu Liu, Zizhen Wang, He Feng, Binghuai Lin, Xuemin Zhao, Yunbo Cao, Zhifang Sui
| Challenge: | DialogUSR is a plug-in and domain-agnostic module that empowers multi-intent detection for chatbots . a single user query triggers inquiries on highspeed train ticket price and weather of destination. |
| Approach: | They propose a dialog utterance splitting and reformulation task that splits multi-intent user query into multiple single-intention sub-queries and recovers all coreferred and omitted information in the sub-questions. |
| Outcome: | The proposed model can be used to split multi-intent user queries into multiple sub-queries . it can be trained in two stages and perform in-depth analyses on the proposed models . |
BanditMTL: Bandit-based Multi-task Learning for Text Classification (2021.acl-long)
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| Challenge: | Existing methods to regularize task variance are unexplored in multi-task text classification. |
| Approach: | They propose a multi-task learning method based on adversarial multi-armed bandit to regularize the task variance by means of a mirror gradient ascent-descent algorithm. |
| Outcome: | The proposed method achieves state-of-the-art in multi-task text classification. |
NeuRAG: End-to-End Neural Knowledge Augmentation via Hyper-Neurons (2026.findings-acl)
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| Challenge: | Existing approaches to grounding large language models in external knowledge are constrained by a decoupled architecture: retrieval and reasoning operate as separate stages, with retrieved text merely prepended as passive context. |
| Approach: | They propose an end-to-end Neuralized RAG framework that unifies knowledge retrieval and fusion through Hyper-Neurons. |
| Outcome: | Extensive experiments across multiple datasets and LLMs demonstrate NeuRAG’s strong and consistent performance as a promising novel RAG paradigm. |
MetaWeighting: Learning to Weight Tasks in Multi-Task Learning (2022.findings-acl)
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| Challenge: | Existing task weighting methods assign weights only based on training losses, while ignoring the gap between the training loss and generalization loss. |
| Approach: | They propose a task weighting algorithm which automatically weights the tasks via a learning-to-learn paradigm and a multi-task text classification paradigm. |
| Outcome: | Extensive experiments show that the proposed method outperforms existing methods in multi-task text classification. |