Sihong Liu


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Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System
Sihong Liu | Jinchao Zhang | Keqing He | Weiran Xu | Jie Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space
Hong Xu | Keqing He | Yuanmeng Yan | Sihong Liu | Zijun Liu | Weiran Xu
Proceedings of the 28th International Conference on Computational Linguistics

Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system. Different from most existing methods that rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this paper, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional distribution on feature spaces of DNNs via Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of whether a test sample belongs to OOD. Experiments on four benchmark datasets show that our method can consistently outperform the baselines.

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Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots
Yuanmeng Yan | Keqing He | Hong Xu | Sihong Liu | Fanyu Meng | Min Hu | Weiran Xu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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. In this paper, we propose a robust adversarial model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. We aim to depart entangled contextual semantics and focus more on the holistic context at the level of the whole sentence. Experiments on two public datasets show that our method consistently outperforms other methods with a statistically significant margin on all the open-vocabulary slots without deteriorating the performance of normal slots.