UniDU: Towards A Unified Generative Dialogue Understanding Framework

• URL: http://arxiv.org/abs/2204.04637v1
• Date: Sun, 10 Apr 2022 09:32:34 GMT
• Title: UniDU: Towards A Unified Generative Dialogue Understanding Framework
• Authors: Zhi Chen, Lu Chen, Bei Chen, Libo Qin, Yuncong Liu, Su Zhu, Jian-Guang Lou, Kai Yu
• Abstract summary: We investigate a unified generative dialogue understanding framework, namely UniDU, to achieve information exchange among DU tasks. We conduct experiments on ten dialogue understanding datasets, which span five fundamental tasks. The proposed UniDU framework outperforms task-specific well-designed methods on all 5 tasks.
• Score: 62.8474841241855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU) direction. However, the current DU approaches just employ an individual model for each DU task, independently, without considering the shared knowledge across different DU tasks. In this paper, we investigate a unified generative dialogue understanding framework, namely UniDU, to achieve information exchange among DU tasks. Specifically, we reformulate the DU tasks into unified generative paradigm. In addition, to consider different training data for each task, we further introduce model-agnostic training strategy to optimize unified model in a balanced manner. We conduct the experiments on ten dialogue understanding datasets, which span five fundamental tasks: dialogue summary, dialogue completion, slot filling, intent detection and dialogue state tracking. The proposed UniDU framework outperforms task-specific well-designed methods on all 5 tasks. We further conduct comprehensive analysis experiments to study the effect factors. The experimental results also show that the proposed method obtains promising performance on unseen dialogue domain.

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