Summary of From Multiple-choice to Extractive Qa: a Case Study For English and Arabic, by Teresa Lynn et al.
From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
by Teresa Lynn, Malik H. Altakrori, Samar Mohamed Magdy, Rocktim Jyoti Das, Chenyang Lyu, Mohamed Nasr, Younes Samih, Kirill Chirkunov, Alham Fikri Aji, Preslav Nakov, Shantanu Godbole, Salim Roukos, Radu Florian, Nizar Habash
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper explores the possibility of repurposing an existing multilingual dataset for a new Natural Language Processing (NLP) task, specifically extractive question answering (EQA) for under-resourced languages. The authors use a subset of the BELEBELE dataset, originally designed for multiple-choice question answering (MCQA), to create a parallel EQA dataset for English and Modern Standard Arabic (MSA). They also present annotation guidelines and provide evaluation results for monolingual and cross-lingual QA pairs in these languages. The authors’ goal is to help others adapt their approach for the remaining 120 BELEBELE language variants, which are currently under-resourced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how we can use an existing language dataset to create a new one that’s better suited for a different task. Right now, there aren’t many datasets for languages that don’t have many speakers or resources. The authors take a subset of a big language dataset and turn it into a new type of dataset that can be used for question-answering tasks in those languages. They also share guidelines on how to label the data correctly and show how well their approach works with English, Arabic, and other languages. Overall, the goal is to help create more language datasets that can benefit people who speak these languages. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Question answering
