Title: Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection,
Abstract: Speaker change detection (SCD) is an important task in dialog modeling. Our
paper addresses the problem of text-based SCD, which differs from existing
audio-based studies and is useful in various scenarios, for example, processing
dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
and enhancing audio SCD with textual information. We formulate text-based SCD
as a matching problem of utterances before and after a certain decision point;
we propose a hierarchical recurrent neural network (RNN) with static
sentence-level attention. Experimental results show that neural networks
consistently achieve better performance than feature-based approaches, and that
our attention-based model significantly outperforms non-attention neural
networks.