# A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots

arXiv: https://arxiv.org/abs/2111.01414 · HTML: https://mlsanity.com/paper/2111.01414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 73 references that are in the map.
- First posted on arXiv 2 Nov 2021.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Deep Reinforcement Learning for Dialogue Generation](https://mlsanity.com/paper/1606.01541/) (2016)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [The Evolved Transformer](https://mlsanity.com/paper/1901.11117/) (2019)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1508.01745/) (2015)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://mlsanity.com/paper/1708.07149/) (2017)
- [Few-shot Natural Language Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2002.12328/) (2020)
- [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/1804.08217/) (2018)
- [Addressing the Rare Word Problem in Neural Machine Translation](https://mlsanity.com/paper/1410.8206/) (2014)
- [How to Build User Simulators to Train RL-based Dialog Systems](https://mlsanity.com/paper/1909.01388/) (2019)
- [Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog](https://mlsanity.com/paper/1908.10719/) (2019)
- [Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model](https://mlsanity.com/paper/1907.02884/) (2019)
- [End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient](https://mlsanity.com/paper/1712.02838/) (2017)
- [Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1801.06176/) (2018)
- [Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation](https://mlsanity.com/paper/2005.07362/) (2020)
- [Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking](https://mlsanity.com/paper/1508.01755/) (2015)
- [Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1811.07550/) (2018)
- [End-to-end LSTM-based dialog control optimized with supervised and reinforcement learning](https://mlsanity.com/paper/1606.01269/) (2016)

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

- [Creating Spoken Dialog Systems in Ultra-Low Resourced Settings](https://mlsanity.com/paper/2312.06266/) (2023)

## Abstract (arXiv)

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards increasingly complex architectures trained on a massive corpus of datasets, like deep learning systems. Motivated with the intuition of resembling human dialogues, progress has been made towards incorporating emotions into the natural language generator, using reinforcement learning. While we see a trend of highly marginal improvement on some metrics, we find that limited justification exists for the metrics, and evaluation practices are not uniform. To conclude, we flag these concerns and highlight possible research directions.