@inproceedings{wang-etal-2022-botsim,
title = "{B}ot{SIM}: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems",
author = "Wang, Guangsen and
Tan, Samson and
Joty, Shafiq and
Wu, Gang and
Au, Jimmy and
Hoi, Steven C.h.",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.18",
doi = "10.18653/v1/2022.emnlp-demos.18",
pages = "178--190",
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 \url{https://tinyurl.com/mryu74cd} and a demo video at \url{https://youtu.be/qLPJm6_UOKY}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems
%A Wang, Guangsen
%A Tan, Samson
%A Joty, Shafiq
%A Wu, Gang
%A Au, Jimmy
%A Hoi, Steven C.h.
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2022-botsim
%X 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.
%R 10.18653/v1/2022.emnlp-demos.18
%U https://aclanthology.org/2022.emnlp-demos.18
%U https://doi.org/10.18653/v1/2022.emnlp-demos.18
%P 178-190
Markdown (Informal)
[BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems](https://aclanthology.org/2022.emnlp-demos.18) (Wang et al., EMNLP 2022)
ACL