# Software-Based Dialogue Systems: Survey, Taxonomy and Challenges

arXiv: https://arxiv.org/abs/2106.10901 · HTML: https://mlsanity.com/paper/2106.10901/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 191 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Learning from Dialogue after Deployment: Feed Yourself, Chatbot!](https://mlsanity.com/paper/1901.05415/) (2019)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Evaluating Quality of Chatbots and Intelligent Conversational Agents](https://mlsanity.com/paper/1704.04579/) (2017)
- [Understanding Chatbot-mediated Task Management](https://mlsanity.com/paper/1802.03109/) (2018)
- [Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time](https://mlsanity.com/paper/1801.02668/) (2018)
- [Towards Automated Customer Support](https://mlsanity.com/paper/1809.00303/) (2018)
- [Programming Bots by Synthesizing Natural Language Expressions into API Invocations](https://mlsanity.com/paper/1711.05410/) (2017)
- [The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes](https://mlsanity.com/paper/1903.12069/) (2019)
- [Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce](https://mlsanity.com/paper/1806.05434/) (2018)
- [Artificial Intelligence based Smart Doctor using Decision Tree Algorithm](https://mlsanity.com/paper/1808.01884/) (2018)
- [Intent Detection and Slots Prompt in a Closed-Domain Chatbot](https://mlsanity.com/paper/1812.10628/) (2018)

## Built on by (most cited first, 11 of 11)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications](https://mlsanity.com/paper/2406.10300/) (2024)
- [Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations](https://mlsanity.com/paper/2312.13871/) (2023)
- [Motivations, Challenges, Best Practices, and Benefits for Bots and Conversational Agents in Software Engineering: A Multivocal Literature Review](https://mlsanity.com/paper/2409.11864/) (2024)
- [IMAD: IMage-Augmented multi-modal Dialogue](https://mlsanity.com/paper/2305.10512/) (2023)
- [Psychological and behavioural responses in human-agent vs. human-human interactions: a systematic review and meta-analysis](https://mlsanity.com/paper/2509.21542/) (2025)
- [An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems](https://mlsanity.com/paper/2309.14391/) (2023)
- [HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs](https://mlsanity.com/paper/2503.04141/) (2025)
- [Democratizing Ethical Assessment of Natural Language Generation Models](https://mlsanity.com/paper/2207.10576/) (2022)
- [Dialogue Systems Engineering: A Survey and Future Directions](https://mlsanity.com/paper/2508.02279/) (2025)
- [Annie, Are You Okay? How Style- and Context-Based Personalization Shape AI-Assisted Decision-Making](https://mlsanity.com/paper/2609.24644/) (2026)

## Abstract (arXiv)

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents' field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.