Bot2Vec: Learning Representations of Chatbots

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


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.
Anthology ID:
S19-1009
Volume:
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venues:
SemEval | *SEM
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
75–84
Language:
URL:
https://aclanthology.org/S19-1009
DOI:
10.18653/v1/S19-1009
Bibkey:
Cite (ACL):
Jonathan Herzig, Tommy Sandbank, Michal Shmueli-Scheuer, and David Konopnicki. 2019. Bot2Vec: Learning Representations of Chatbots. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 75–84, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Bot2Vec: Learning Representations of Chatbots (Herzig et al., SemEval-*SEM 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/S19-1009.pdf