Summary of Multilingual Non-factoid Question Answering with Answer Paragraph Selection, by Ritwik Mishra et al.
Multilingual Non-Factoid Question Answering with Answer Paragraph Selection
by Ritwik Mishra, Sreeram Vennam, Rajiv Ratn Shah, Ponnurangam Kumaraguru
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 presents MuNfQuAD, a large-scale multilingual Question Answering (QA) dataset featuring non-factoid questions. Unlike existing datasets, which focus on factoid-based short-context QA in high-resource languages, MuNfQuAD is designed for low-resource languages, comprising over 578K QA pairs across 38 languages. The dataset utilizes interrogative sub-headings from BBC news articles as questions and corresponding paragraphs as silver answers. To evaluate the effectiveness of MuNfQuAD, the paper fine-tunes an Answer Paragraph Selection (APS) model, achieving state-of-the-art results on both the testset and golden set. The study also explores the APS model’s ability to generalize across languages and reduce context complexity. Overall, MuNfQuAD offers a valuable resource for the QA research community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big database of questions and answers in many different languages. It’s special because most existing databases are only for English or other popular languages. The new database is called MuNfQuAD and it has over 578,000 pairs of questions and answers. To test how well the database works, scientists fine-tuned an algorithm to answer the questions correctly. They found that this algorithm was very good at answering the questions, even when it only had information from a single language. The new database is important because it will help scientists improve artificial intelligence’s ability to understand human language. |
Keywords
* Artificial intelligence * Question answering
