Summary of A Survey on Recent Advances in Conversational Data Generation, by Heydar Soudani et al.
A Survey on Recent Advances in Conversational Data Generation
by Heydar Soudani, Roxana Petcu, Evangelos Kanoulas, Faegheh Hasibi
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of multi-turn conversational data generation techniques, focusing on three types of dialogue systems: open domain, task-oriented, and information-seeking. The authors categorize existing research based on key components like seed data creation, utterance generation, and quality filtering methods, introducing a general framework that outlines the main principles of conversation data generation systems. Additionally, the paper examines evaluation metrics and methods for assessing synthetic conversational data, addresses current challenges in the field, and explores potential directions for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make computers talk like humans. Right now, making computers have conversations is hard because we don’t have enough good examples of human-computer conversations. In the past, people created these examples by doing tasks like crowd-sourcing or creating them themselves. However, this method takes a lot of time and money. As an alternative, scientists are using techniques to create fake conversation data that can be used to train computers. The paper looks at three types of computer-human conversations: open-ended conversations, task-based conversations, and information-seeking conversations. It also talks about the different ways scientists generate this fake conversation data, evaluate how well it works, and what challenges they’re facing. The goal is to help researchers and people who work with computers understand what’s already been done in this area and where future research could go. |
