Measuring Semantic Coherence of a Conversation. Vakulenko, S., de Rijke, M., Cochez, M., Savenkov, V., & Polleres, A. In Vrandečić, D., Bontcheva, K., Suárez-Figueroa, M. C., Presutti, V., Celino, I., Sabou, M., Kaffee, L., & Simperl, E., editors, Proceedings of the 17th International Semantic Web Conference (ISWC 2018), volume 11136, of Lecture Notes in Computer Science (LNCS), pages 634–651, Monterey, CA, October, 2018. Springer.
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Paper doi abstract bibtex
Paper doi abstract bibtex Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. In this paper, we introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate in our evaluation results how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.
@inproceedings{vaku-etal-2018ISWC,
author = {Svitlana Vakulenko and Maarten de Rijke and Michael Cochez and Vadim Savenkov and Axel Polleres},
title = {Measuring Semantic Coherence of a Conversation},
abstract = {Conversational systems have become increasingly popular as a way for humans to interact with computers. To be able to provide intelligent responses, conversational systems must correctly model the structure and semantics of a conversation. In this paper, we introduce the task of measuring semantic (in)coherence in a conversation with respect to background knowledge, which relies on the identification of semantic relations between concepts introduced during a conversation. We propose and evaluate graph-based and machine learning approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate in our evaluation results how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.},
month = oct,
day = {8--12},
pages = {634--651},
editor = {Denny Vrande\v{c}i{\'c} and Kalina Bontcheva and Mari Carmen Su{\'a}rez-Figueroa and Valentina Presutti and Irene Celino and Marta Sabou and Lucie-Aim{\'e}e Kaffee and Elena Simperl},
doi = {10.1007/978-3-030-00671-6_37},
year = 2018,
booktitle = {Proceedings of the 17th International Semantic Web Conference (ISWC 2018)},
address = {Monterey, CA},
series = LNCS,
volume = 11136,
publisher = {Springer},
url = {http://polleres.net/publications/vaku-etal-2018ISWC.pdf},
}Downloads: 0
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