In human-computer conversation systems, the context of a userissued utterance is particularly important because it provides useful background information of the conversation. However, it is unwise to track all previous utterances in the current session as not all of them are equally important. In this paper, we address the problem of session segmentation. We propose an embedding-enhanced TextTiling approach, inspired by the observation that conversation utterances are highly noisy, and that word embeddings provide a robust way of capturing semantics. Experimental results show that our approach achieves better performance than the TextTiling, MMD approaches.
In our previous study, we address the problem of session segmentation in text-based human-computer conversations REF .
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
ArXiv
ArXiv
Computer Science