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Measuring semantic coherence of a conversation

: Vakulenko, S.; Rijke, M. de; Cochez, M.; Savenkov, V.; Polleres, A.


Vrandečić, D.:
The Semantic Web - ISWC 2018. 17th International Semantic Web Conference. Proceedings. Pt.I : Monterey, CA, USA, October 8-12, 2018
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11136)
ISBN: 978-3-030-00670-9 (Print)
ISBN: 978-3-030-00671-6 (Online)
ISBN: 978-3-030-00672-3
ISBN: 3-030-00670-0
International Semantic Web Conference (ISWC) <17, 2018, Monterey/Calif.>
Conference Paper
Fraunhofer FIT ()

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. 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-based approaches for measuring semantic coherence using knowledge graphs, their vector space embeddings and word embedding models, as sources of background knowledge. We demonstrate how these approaches are able to uncover different coherence patterns in conversations on the Ubuntu Dialogue Corpus.