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arXiv · 2211.11982

BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems

Guangsen Wang·Samson Tan·Shafiq Joty·Gang Wu·Jimmy Au·Steven Hoi
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Abstract

We present BotSIM, a data-efficient end-to-end Bot SIMulation toolkit for commercial text-based task-oriented dialog (TOD) systems. BotSIM consists of three major components: 1) a Generator that can infer semantic-level dialog acts and entities from bot definitions and generate user queries via model-based paraphrasing; 2) an agenda-based dialog user Simulator (ABUS) to simulate conversations with the dialog agents; 3) a Remediator to analyze the simulated conversations, visualize the bot health reports and provide actionable remediation suggestions for bot troubleshooting and improvement. We demonstrate BotSIM's effectiveness in end-to-end evaluation, remediation and multi-intent dialog generation via case studies on two commercial bot platforms. BotSIM's "generation-simulation-remediation" paradigm accelerates the end-to-end bot evaluation and iteration process by: 1) reducing manual test cases creation efforts; 2) enabling a holistic gauge of the bot in terms of NLU and end-to-end performance via extensive dialog simulation; 3) improving the bot troubleshooting process with actionable suggestions. A demo of our system can be found at https://tinyurl.com/mryu74cd and a demo video at https://youtu.be/qLi5iSoly30. We have open-sourced the toolkit at https://github.com/salesforce/botsim

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Guangsen Wang, Samson Tan, Shafiq Joty, Gang Wu, Jimmy Au, Steven Hoi. 2022-11-22. BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems. https://arxiv.org/abs/2211.11982

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