Measuring semantic coherence of a conversation

Svitlana Vakulenko, Maarten de Rijke, Michael Cochez, Vadim Savenkov, Axel Polleres*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2018 - 17th International Semantic Web Conference, 2018, Proceedings
EditorsMari Carmen Suárez-Figueroa, Valentina Presutti, Lucie-Aimee Kaffee, Elena Simperl, Marta Sabou, Denny Vrandecic, Irene Celino, Kalina Bontcheva
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783030006709
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event17th International Semantic Web Conference, ISWC 2018 - Monterey, United States
Duration: 8 Oct 201812 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11136 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Semantic Web Conference, ISWC 2018
Country/TerritoryUnited States


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