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
Abstract
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 https://tinyurl.com/mryu74cd and a demo video at https://youtu.be/qLPJm6_UOKY.- Anthology ID:
- 2022.emnlp-demos.18
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Editors:
- Wanxiang Che, Ekaterina Shutova
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 178–190
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-demos.18
- DOI:
- 10.18653/v1/2022.emnlp-demos.18
- Cite (ACL):
- Guangsen Wang, Samson Tan, Shafiq Joty, Gang Wu, Jimmy Au, and Steven C.h. Hoi. 2022. BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 178–190, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems (Wang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-demos.18.pdf