Papers by Sihong Liu
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
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| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
| Approach: | They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on multi-task dialogues. |
Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots (2020.emnlp-main)
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| Challenge: | Open-vocabulary slots degrade neural-based slot filling models because they can take on unlimited set of values and have no semantic restriction nor length limit. |
| Approach: | They propose a model-agnostic slot filling method that explicitly decouples local semantics inherent in open-vocabulary slot words from the global context. |
| Outcome: | The proposed method outperforms other models on open-vocabulary slots without deteriorating performance. |
A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space (2020.coling-main)
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| Challenge: | Existing methods for detecting out-of-domain (OOD) intents rely on manually labeled samples . a strong generative distance-based classifier can detect OOD samples in task-oriented dialog systems . |
| Approach: | They propose a generative distance-based classifier to detect out-of-domain (OOD) intents . they use Gaussian discriminant analysis to avoid over-confidence problems . |
| Outcome: | The proposed method outperforms baseline methods on four benchmark datasets. |