# Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use

arXiv: https://arxiv.org/abs/1907.07638 · HTML: https://mlsanity.com/paper/1907.07638/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Toward Self-learning End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2201.06849/) (2022)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis](https://mlsanity.com/paper/2109.08412/) (2021)
- [Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs](https://mlsanity.com/paper/2601.09365/) (2026)
- [Causal Inference for Chatting Handoff](https://mlsanity.com/paper/2210.02862/) (2022)

## Abstract (arXiv)

Neural end-to-end goal-oriented dialog systems showed promise to reduce the workload of human agents for customer service, as well as reduce wait time for users. However, their inability to handle new user behavior at deployment has limited their usage in real world. In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently. The proposed method has three goals: 1) maximize user's task success by transferring to human agents, 2) minimize the load on the human agents by transferring to them only when it is essential and 3) learn online from the human agent's responses to reduce human agents load further. We evaluate our proposed method on a modified-bAbI dialog task that simulates the scenario of new user behaviors occurring at test time. Experimental results show that our proposed method is effective in achieving the desired goals.