Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical RNN with Static Sentence-Level Attention for Text-Based
  Speaker Change Detection   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.

8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int