The following ISWC 2018 paper on measuring semantic coherence of a conversation is online now:

  • Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, and Axel Polleres. Measuring semantic coherence of a conversation. In ISWC 2018: 17th International Semantic Web Conference, page 634–651. Springer, October 2018. Bibtex, PDF
    Author = {Vakulenko, Svitlana and de Rijke, Maarten and Cochez, Michael and Savenkov, Vadim and Polleres, Axel},
    Booktitle = {ISWC 2018: 17th International Semantic Web Conference},
    Date-Added = {2018-05-26 04:41:16 +0000},
    Date-Modified = {2018-10-27 09:32:14 +0200},
    Month = {October},
    Pages = {634--651},
    Publisher = {Springer},
    Title = {Measuring semantic coherence of a conversation},
    Year = {2018}}

Conversational systems have become increasingly popular as a way for people 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.