DSBERT:Unsupervised Dialogue Structure learning with BERT
This work addresses the high cost of manual dialogue structure design for developers, enabling automatic extraction to improve dialogue systems, though it appears incremental as it builds on existing methods like BERT and AutoEncoder.
The authors tackled unsupervised dialogue structure learning by proposing DSBERT, a BERT-based algorithm combined with AutoEncoder and balanced loss functions, which generated a dialogue structure closer to the real one and effectively distinguished sentences with different semantics.
Unsupervised dialogue structure learning is an important and meaningful task in natural language processing. The extracted dialogue structure and process can help analyze human dialogue, and play a vital role in the design and evaluation of dialogue systems. The traditional dialogue system requires experts to manually design the dialogue structure, which is very costly. But through unsupervised dialogue structure learning, dialogue structure can be automatically obtained, reducing the cost of developers constructing dialogue process. The learned dialogue structure can be used to promote the dialogue generation of the downstream task system, and improve the logic and consistency of the dialogue robot's reply.In this paper, we propose a Bert-based unsupervised dialogue structure learning algorithm DSBERT (Dialogue Structure BERT). Different from the previous SOTA models VRNN and SVRNN, we combine BERT and AutoEncoder, which can effectively combine context information. In order to better prevent the model from falling into the local optimal solution and make the dialogue state distribution more uniform and reasonable, we also propose three balanced loss functions that can be used for dialogue structure learning. Experimental results show that DSBERT can generate a dialogue structure closer to the real structure, can distinguish sentences with different semantics and map them to different hidden states.