# A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences

arXiv: https://arxiv.org/abs/2203.16891 · HTML: https://mlsanity.com/paper/2203.16891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 74 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://mlsanity.com/paper/2004.08449/) (2020)
- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Directed Acyclic Graph Network for Conversational Emotion Recognition](https://mlsanity.com/paper/2105.12907/) (2021)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [Towards Persona-Based Empathetic Conversational Models](https://mlsanity.com/paper/2004.12316/) (2020)
- [Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study](https://mlsanity.com/paper/1906.01603/) (2019)
- [Hierarchical Pre-training for Sequence Labelling in Spoken Dialog](https://mlsanity.com/paper/2009.11152/) (2020)
- [Pretraining Methods for Dialog Context Representation Learning](https://mlsanity.com/paper/1906.00414/) (2019)
- [DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2008.06914/) (2020)
- [Guiding attention in Sequence-to-sequence models for Dialogue Act prediction](https://mlsanity.com/paper/2002.08801/) (2020)
- [Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour](https://mlsanity.com/paper/1801.06055/) (2018)
- [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://mlsanity.com/paper/1709.04250/) (2017)
- [From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining](https://mlsanity.com/paper/1908.11216/) (2019)
- [Sequential Dialogue Context Modeling for Spoken Language Understanding](https://mlsanity.com/paper/1705.03455/) (2017)
- [Probing Neural Dialog Models for Conversational Understanding](https://mlsanity.com/paper/2006.08331/) (2020)
- [Utterance-level Dialogue Understanding: An Empirical Study](https://mlsanity.com/paper/2009.13902/) (2020)
- [Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks](https://mlsanity.com/paper/2109.09366/) (2021)
- [A Dual-Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification](https://mlsanity.com/paper/1810.09154/) (2018)
- [Report from the NSF Future Directions Workshop, Toward User-Oriented Agents: Research Directions and Challenges](https://mlsanity.com/paper/2006.06026/) (2020)
- [Domain adversarial learning for emotion recognition](https://mlsanity.com/paper/1910.13807/) (2019)

## Abstract (arXiv)

Some exciting new approaches to neural architectures for the analysis of conversation have been introduced over the past couple of years. These include neural architectures for detecting emotion, dialogue acts, and sentiment polarity. They take advantage of some of the key attributes of contemporary machine learning, such as recurrent neural networks with attention mechanisms and transformer-based approaches. However, while the architectures themselves are extremely promising, the phenomena they have been applied to to date are but a small part of what makes conversation engaging. In this paper we survey these neural architectures and what they have been applied to. On the basis of the social science literature, we then describe what we believe to be the most fundamental and definitional feature of conversation, which is its co-construction over time by two or more interlocutors. We discuss how neural architectures of the sort surveyed could profitably be applied to these more fundamental aspects of conversation, and what this buys us in terms of a better analysis of conversation and even, in the longer term, a better way of generating conversation for a conversational system.