Dialogue Session Segmentation by Embedding-Enhanced TextTiling
This addresses the problem of efficiently tracking relevant context in dialogue systems for improved user interaction, but it is incremental as it builds on existing TextTiling with embeddings.
The paper tackles session segmentation in human-computer conversation systems by proposing an embedding-enhanced TextTiling method to handle noisy utterances, achieving better performance than TextTiling and MMD approaches.
In human-computer conversation systems, the context of a user-issued 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.