# Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems Source: [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:06:39.153Z Authors: Derek Chen, Howard Chen, Yi Yang, Alex Lin, Zhou Yu Published: 2021-04-01T22:04:25.000Z ## Abstract Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success. We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines involving large-scale, pre-trained language models on this dataset. Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Cascading Dialogue Success on ABCD (test)](): Cascading Dialogue Success; ABCD (test); Intent Accuracy: 90.5. - [Action State Tracking on ABCD (test)](): Action State Tracking; ABCD (test); B-Slot Score: 0.936. ## Original sources and implementation [Original paper]() [PDF]()