# An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction

arXiv: https://arxiv.org/abs/2106.14611 · HTML: https://mlsanity.com/paper/2106.14611/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling](https://mlsanity.com/paper/1804.09160/) (2018)
- [MA-DST: Multi-Attention Based Scalable Dialog State Tracking](https://mlsanity.com/paper/2002.08898/) (2020)
- [Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning](https://mlsanity.com/paper/1812.03509/) (2018)

## Built on by (most cited first, 3 of 3)

- [Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic Learning](https://mlsanity.com/paper/2212.10087/) (2022)
- [A Robust Semantic Frame Parsing Pipeline on a New Complex Twitter Dataset](https://mlsanity.com/paper/2212.08987/) (2022)
- [Neural Coreference Resolution based on Reinforcement Learning](https://mlsanity.com/paper/2212.09028/) (2022)

## Abstract (arXiv)

Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/add/remove slot values through multiround interactions with users. In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values. We perform two experiments on the benchmark ATIS dataset and demonstrate that the new system can improve parsing performance by at least 2.5% in terms of F1, with only one round of feedback. The improvement becomes even larger when the number of feedback rounds increases. Furthermore, we also compare the new system with state-of-the-art dialogue state tracking systems and demonstrate that the new interactive system can perform better on multiround spoken language understanding tasks in terms of slot- and sentence-level accuracy.