Dazhen Wan


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ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Qi Zhu | Christian Geishauser | Hsien-chin Lin | Carel van Niekerk | Baolin Peng | Zheng Zhang | Shutong Feng | Michael Heck | Nurul Lubis | Dazhen Wan | Xiaochen Zhu | Jianfeng Gao | Milica Gasic | Minlie Huang
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short in delivering comprehensive arrays of data, model, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.


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A Unified Dialogue User Simulator for Few-shot Data Augmentation
Dazhen Wan | Zheng Zhang | Qi Zhu | Lizi Liao | Minlie Huang
Findings of the Association for Computational Linguistics: EMNLP 2022

Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.


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Robustness Testing of Language Understanding in Task-Oriented Dialog
Jiexi Liu | Ryuichi Takanobu | Jiaxin Wen | Dazhen Wan | Hongguang Li | Weiran Nie | Cheng Li | Wei Peng | Minlie Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Most language understanding models in task-oriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution. However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice. In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in real-world dialog systems, namely, language variety, speech characteristics, and noise perturbation. We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in task-oriented dialog. Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models. The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.