A Hybrid Framework for Topic Structure using Laughter Occurrences
This work addresses topic segmentation in spoken dialogs for applications like online understanding, but it is incremental as it builds on existing methods.
The paper tackled the problem of identifying discourse-level topic structures in multiparty conversations by integrating paralinguistic laughter occurrences with lexical cohesion, resulting in a hybrid framework that improved the performance of both standalone approaches.
Conversational discourse coherence depends on both linguistic and paralinguistic phenomena. In this work we combine both paralinguistic and linguistic knowledge into a hybrid framework through a multi-level hierarchy. Thus it outputs the discourse-level topic structures. The laughter occurrences are used as paralinguistic information from the multiparty meeting transcripts of ICSI database. A clustering-based algorithm is proposed that chose the best topic-segment cluster from two independent, optimized clusters, namely, hierarchical agglomerative clustering and $K$-medoids. Then it is iteratively hybridized with an existing lexical cohesion based Bayesian topic segmentation framework. The hybrid approach improves the performance of both of the stand-alone approaches. This leads to the brief study of interactions between topic structures with discourse relational structure. This training-free topic structuring approach can be applicable to online understanding of spoken dialogs.