Challenge: evaluating and troubleshooting production TOD systems is still a largely manual process requiring large amount of human conversations with the systems.
Approach: They propose a data-efficient end-to-end Bot SIMulation framework for commercial task-oriented dialog systems that can generate user queries and generate semantic-level dialog acts and entities from bot definitions.
Outcome: The proposed framework is able to infer semantic-level dialog acts and entities from bot definitions and generate user queries via model-based paraphrasing.

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Challenge: Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system.
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Challenge: Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale.
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Challenge: SDialog is an open-source Python toolkit for end-to-end development, simulation, evaluation and analysis of LLM-based conversational agents.
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Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)

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Challenge: Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results .
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End-to-End Task-Oriented Dialogue Systems Based on Schema (2023.findings-acl)

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Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems (2020.emnlp-main)

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Challenge: Lack of time efficient and reliable evalu-ation methods is hampering the development of conversational dialogue systems (chatbots).
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