Tommy Sandbank


2019

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Bot2Vec: Learning Representations of Chatbots
Jonathan Herzig | Tommy Sandbank | Michal Shmueli-Scheuer | David Konopnicki
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

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.

2018

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Detecting Egregious Conversations between Customers and Virtual Agents
Tommy Sandbank | Michal Shmueli-Scheuer | Jonathan Herzig | David Konopnicki | John Richards | David Piorkowski
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.

2017

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Neural Response Generation for Customer Service based on Personality Traits
Jonathan Herzig | Michal Shmueli-Scheuer | Tommy Sandbank | David Konopnicki
Proceedings of the 10th International Conference on Natural Language Generation

We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.