Summary of Developing Pugg For Polish: a Modern Approach to Kbqa, Mrc, and Ir Dataset Construction, by Albert Sawczyn et al.
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
by Albert Sawczyn, Katsiaryna Viarenich, Konrad Wojtasik, Aleksandra Domogała, Marcin Oleksy, Maciej Piasecki, Tomasz Kajdanowicz
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This paper proposes a semi-automated approach to create knowledge base question answering (KBQA) datasets for low-resource languages, focusing on Polish as the primary target language. The authors design and implement a pipeline that incorporates tasks like machine reading comprehension and information retrieval to reduce human labor. They introduce the PUGG dataset, a novel KBQA dataset for Polish, alongside MRC and IR datasets. The paper also provides implementation details, findings, statistics, and evaluations of baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers aim to address the significant gap in KBQA datasets for low-resource languages by leveraging Large Language Models (LLMs) to reduce human workload. Their approach combines tasks like machine reading comprehension and information retrieval to create a comprehensive pipeline. The PUGG dataset is introduced as the first Polish KBQA dataset, alongside novel MRC and IR datasets. |
Keywords
* Artificial intelligence * Knowledge base * Question answering
