# An empirical assessment of deep learning approaches to task-oriented dialog management

arXiv: https://arxiv.org/abs/2108.03478 · HTML: https://mlsanity.com/paper/2108.03478/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 7 Aug 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Scaling Multi-Domain Dialogue State Tracking via Query Reformulation](https://mlsanity.com/paper/1903.05164/) (2019)
- [Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management](https://mlsanity.com/paper/1707.00130/) (2017)
- [Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking](https://mlsanity.com/paper/1508.01755/) (2015)
- [Multi-domain Dialog State Tracking using Recurrent Neural Networks](https://mlsanity.com/paper/1506.07190/) (2015)
- [Natural Language Generation by Hierarchical Decoding with Linguistic Patterns](https://mlsanity.com/paper/1808.02747/) (2018)
- [Neural Models for Sequence Chunking](https://mlsanity.com/paper/1701.04027/) (2017)
- [Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems](https://mlsanity.com/paper/1508.03391/) (2015)
- [Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1604.00117/) (2016)
- [A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots](https://mlsanity.com/paper/1908.10398/) (2019)

## Built on by (most cited first, 1 of 1)

- [Dynamically Retrieving Knowledge via Query Generation for Informative Dialogue Generation](https://mlsanity.com/paper/2208.00128/) (2022)

## Abstract (arXiv)

Deep learning is providing very positive results in areas related to conversational interfaces, such as speech recognition, but its potential benefit for dialog management has still not been fully studied. In this paper, we perform an assessment of different configurations for deep-learned dialog management with three dialog corpora from different application domains and varying in size, dimensionality and possible system responses. Our results have allowed us to identify several aspects that can have an impact on accuracy, including the approaches used for feature extraction, input representation, context consideration and the hyper-parameters of the deep neural networks employed.