Summary of Mcsqa: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans, By Yusuke Sakai et al.
mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans
by Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| 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 proposes Multilingual CommonsenseQA (mCSQA), a dataset designed to evaluate the natural language understanding capabilities of multilingual language models. Current multilingual datasets are created through translation, which cannot assess language-specific aspects. mCSQA leverages language models to generate questions and answers, reducing human efforts for verification. The constructed dataset serves as a benchmark for cross-lingual language-transfer capabilities of multilingual LMs. Experimental results show high transfer capabilities for easy-to-solve questions but lower capabilities for questions requiring deep knowledge or commonsense. This highlights the need for language-specific datasets for evaluation and training. The proposed method demonstrates that multilingual LMs can create QA including language-specific knowledge, significantly reducing dataset creation cost compared to manual creation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a special kind of dataset to help us understand how well computer programs can understand different languages. Right now, we have datasets created by translating text from one language to another, but these don’t show if the program really understands what’s being said. The researchers propose a new way to make this dataset using computer programs themselves. They generate questions and answers that require common sense or specialized knowledge, then use humans to check if they’re correct. This helps us see how well language programs can transfer their skills from one language to another. The results show that these programs are good at answering easy questions but struggle with harder ones. This means we need special datasets like this to really understand what our computer programs can do. |
Keywords
» Artificial intelligence » Language understanding » Translation
