Guangsen Wang


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 C.h. Hoi
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present BotSIM, a data-efficient end-to-end Bot SIMulation framework for commercial 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 and a demo video at