Summary of Jmultiwoz: a Large-scale Japanese Multi-domain Task-oriented Dialogue Dataset, by Atsumoto Ohashi et al.
JMultiWOZ: A Large-Scale Japanese Multi-Domain Task-Oriented Dialogue Dataset
by Atsumoto Ohashi, Ryu Hirai, Shinya Iizuka, Ryuichiro Higashinaka
First submitted to arxiv on: 26 Mar 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 The paper introduces JMultiWOZ, the first large-scale multi-domain task-oriented dialogue dataset for Japanese language research. It aims to advance the development of task-oriented dialogue systems in Japanese by providing a benchmark comparable to MultiWOZ2.2. The authors construct JMultiWOZ and evaluate its performance using state-of-the-art methods on MultiWOZ2.2 and large language models (LLMs). Results show that JMultiWOZ is on par with MultiWOZ2.2, highlighting the potential of LLMs in Japanese. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new Japanese dialogue dataset to help improve chatbots that can have conversations about different topics. It compares this new dataset to an existing English one and shows how well certain language models perform on it. The results suggest that these models are almost as good at understanding and responding to Japanese conversations as they are for English. |
