Bot2Vec: Learning representations of chatbots

Jonathan Herzig, Tommy Sandbank, Michal Shmueli-Scheuer, David Konopnicki, John Richards

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

Abstract

Chatbots (i.e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms. These platforms are equipped with novel testing tools aimed at improving the quality of individual chatbots. Doing so requires an understanding of what sort of bots are being built (captured by their underlying conversation graphs) and how well they perform (derived through analysis of conversation logs). In this paper, we propose a new model, BOT2VEC, that embeds bots to a compact representation based on their structure and usage logs. Then, we utilize BOT2VEC representations to improve the quality of two bot analysis tasks. Using conversation data and graphs of over than 90 bots, we show that BOT2VEC representations improve detection performance by more than 16% for both tasks.

Original languageEnglish
Title of host publication*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics
PublisherAssociation for Computational Linguistics (ACL)
Pages75-84
Number of pages10
ISBN (Electronic)9781948087933
StatePublished - 2019
Externally publishedYes
Event8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019 - Minneapolis, United States
Duration: 6 Jun 20197 Jun 2019

Publication series

Name*SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics

Conference

Conference8th Joint Conference on Lexical and Computational Semantics, *SEM@NAACL-HLT 2019
Country/TerritoryUnited States
CityMinneapolis
Period6/06/197/06/19

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