Semantic Features for Dialogue Act Recognition
3rd International Conference on Statistical Language and Speech Processing (SLSP 2015) (2015)
Abstract
Dialogue act recognition commonly relies on lexical, syntactic, prosodic and/or dialogue history based features. However, few approaches exploit semantic information. The main goal of this paper is thus to propose semantic features and integrate them into a dialogue act recognition task to improve the recognition score. Three different feature computation approaches are proposed, evaluated and compared: Latent Dirichlet Allocation and the HAL and COALS semantic spaces. An interesting contribution is that all the features are created without any supervision. These approaches are evaluated on a Czech dialogue corpus. We experimentally show that all proposed approaches significantly improve the recognition accuracy.
Authors
BibTex
@inproceedings{kral2015semantic, title = {Semantic Features for Dialogue Act Recognition}, author = {Kr{\'a}l, Pavel and Lenc, Ladislav and Cerisara, Christophe}, booktitle = {3rd International Conference on Statistical Language and Speech Processing (SLSP 2015)}, pages = {153-163}, month = {November 24-26}, year = {2015}, address = {Budapest, Hungary}, isbn = {978-3-319-25788-4}, doi = {10.1007/978-3-319-25789-1_15}, publisher = {Springer}, organization = {Springer} }
