Summary of Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-resource User Groups, by Zhiyang Qi and Michimasa Inaba
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups
by Zhiyang Qi, Michimasa Inaba
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 This study tackles the challenges faced by spoken dialogue systems (SDSs) when interacting with users exhibiting distinct conversational behaviors, particularly minors. The authors propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources. Leveraging a large language model (LLM) and pre-trained language model (PLM), this method generates enriched and personalized dialogue data, enabling improved interactions with unique user demographics. Extensive experiments validate the efficacy of the methodology, highlighting its potential to foster more adaptive and inclusive dialogue systems. The proposed framework utilizes speaker styles extracted from an LLM and simulates dialogue act history using a PLM to generate high-quality, diverse training data. This approach can significantly improve SDS performance in scenarios where data are scarce, ultimately contributing to more effective human-machine interactions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how computers talk to people, especially kids. Right now, these computer systems (called spoken dialogue systems) have trouble understanding people who speak differently or don’t have much information. The researchers came up with a new way to make the computer systems better by giving them more practice talking like real people do. They used big language models to understand how people talk and simulate conversations that might happen in real life. This helped the computer systems get better at understanding different people and having more natural conversations. |
Keywords
» Artificial intelligence » Data augmentation » Language model » Large language model
