# Recent Progress in Conversational AI

arXiv: https://arxiv.org/abs/2204.09719 · HTML: https://mlsanity.com/paper/2204.09719/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 46 of its 79 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (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)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [End-to-End Task-Completion Neural Dialogue Systems](https://mlsanity.com/paper/1703.01008/) (2017)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/1704.03084/) (2017)
- [Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access](https://mlsanity.com/paper/1609.00777/) (2016)
- [Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation](https://mlsanity.com/paper/1607.00970/) (2016)
- [Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems](https://mlsanity.com/paper/1511.06931/) (2015)
- [End-to-End Neural Transformer Based Spoken Language Understanding](https://mlsanity.com/paper/2008.10984/) (2020)
- [BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1711.05715/) (2017)
- [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017)
- [Neural Generative Question Answering](https://mlsanity.com/paper/1512.01337/) (2015)
- [Flexible End-to-End Dialogue System for Knowledge Grounded Conversation](https://mlsanity.com/paper/1709.04264/) (2017)

## Abstract (arXiv)

Conversational artificial intelligence (AI) is becoming an increasingly popular topic among industry and academia. With the fast development of neural network-based models, a lot of neural-based conversational AI system are developed. We will provide a brief review of the recent progress in the Conversational AI, including the commonly adopted techniques, notable works, famous competitions from academia and industry and widely used datasets.