1 code implementation • EMNLP 2021 • Yuanmeng Yan, Rumei Li, Sirui Wang, Hongzhi Zhang, Zan Daoguang, Fuzheng Zhang, Wei Wu, Weiran Xu
The key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB).
no code implementations • EMNLP 2020 • Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, Weiran Xu
Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
no code implementations • Findings (EMNLP) 2021 • Yuejie Lei, Fujia Zheng, Yuanmeng Yan, Keqing He, Weiran Xu
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding.
Abstractive Dialogue Summarization
Abstractive Text Summarization
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1 code implementation • NAACL 2022 • Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
no code implementations • 17 Oct 2022 • Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Yuanmeng Yan, Weiran Xu
In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.
1 code implementation • 13 Sep 2022 • Zhen Yang, Fandong Meng, Yuanmeng Yan, Jie zhou
While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with the human judgement on whether the word is well or poorly translated.
1 code implementation • 8 Mar 2022 • LiWen Wang, Rumei Li, Yang Yan, Yuanmeng Yan, Sirui Wang, Wei Wu, Weiran Xu
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
1 code implementation • EMNLP 2021 • LiWen Wang, Xuefeng Li, Jiachi Liu, Keqing He, Yuanmeng Yan, Weiran Xu
Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research.
1 code implementation • NAACL 2021 • Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Hong Xu, Weiran Xu
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.
1 code implementation • NAACL 2021 • LiWen Wang, Yuanmeng Yan, Keqing He, Yanan Wu, Weiran Xu
In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.
1 code implementation • ACL 2021 • Yanan Wu, Zhiyuan Zeng, Keqing He, Hong Xu, Yuanmeng Yan, Huixing Jiang, Weiran Xu
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set.
1 code implementation • ACL 2021 • Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, Weiran Xu
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
1 code implementation • ACL 2021 • Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, Weiran Xu
Learning high-quality sentence representations benefits a wide range of natural language processing tasks.
no code implementations • COLING 2020 • Hong Xu, Keqing He, Yuanmeng Yan, Sihong Liu, Zijun Liu, Weiran Xu
Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system.
no code implementations • COLING 2020 • Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie zhou
In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling.
no code implementations • ACL 2020 • Keqing He, Yuanmeng Yan, Weiran Xu
Neural-based context-aware models for slot tagging have achieved state-of-the-art performance.