# Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems

arXiv: https://arxiv.org/abs/1703.07055 · HTML: https://mlsanity.com/paper/1703.07055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI](https://mlsanity.com/paper/2107.13076/) (2021)
- [Fuzzy Classification of Multi-intent Utterances](https://mlsanity.com/paper/2104.10830/) (2021)
- [Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging](https://mlsanity.com/paper/2105.10267/) (2021)
- [Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding](https://mlsanity.com/paper/2303.01341/) (2023)

## Abstract (arXiv)

Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. In addition, our experiments demonstrate that the reinforcement learning based dialogue system is able to learn when and what to confirm in order to achieve better performance and greater robustness.