mlsanityData to 14 Oct 2026

Pir'a: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean

arXiv 2202.02398 ↗First posted 4 Feb 2022cs.CLcited by 4

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Abstract (arXiv)

Current research in natural language processing is highly dependent on carefully produced corpora. Most existing resources focus on English; some resources focus on languages such as Chinese and French; few resources deal with more than one language. This paper presents the Pir'a dataset, a large set of questions and answers about the ocean and the Brazilian coast both in Portuguese and English. Pir'a is, to the best of our knowledge, the first QA dataset with supporting texts in Portuguese, and, perhaps more importantly, the first bilingual QA dataset that includes this language. The Pir'a dataset consists of 2261 properly curated question/answer (QA) sets in both languages. The QA sets were manually created based on two corpora: abstracts related to the Brazilian coast and excerpts of United Nation reports about the ocean. The QA sets were validated in a peer-review process with the dataset contributors. We discuss some of the advantages as well as limitations of Pir'a, as this new resource can support a set of tasks in NLP such as question-answering, information retrieval, and machine translation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 38 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

22 of 38 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  5. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  7. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,605
  8. 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,554
  9. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  10. 2018Annotation Artifacts in Natural Language Inference Datacited by 671
  11. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 578
  12. 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 488
10 more
  1. 2018QuAC : Question Answering in Contextcited by 381
  2. 2019MLQA: Evaluating Cross-lingual Extractive Question Answeringcited by 340
  3. 2020XOR QA: Cross-lingual Open-Retrieval Question Answeringcited by 121
  4. 2017DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applicationscited by 110
  5. 2020FQuAD: French Question Answering Datasetcited by 58
  6. 2021GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrievalcited by 39
  7. 2017Making Neural QA as Simple as Possible but not Simplercited by 33
  8. 2017Dataset for the First Evaluation on Chinese Machine Reading Comprehensioncited by 9
  9. 2016SelQA: A New Benchmark for Selection-based Question Answeringcited by 6
  10. 2018MilkQA: a Dataset of Consumer Questions for the Task of Answer Selectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2023Benchmarks for Pir'a 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Changecited by 1
  2. 2022Tracking environmental policy changes in the Brazilian Federal Official Gazettecited by 0
  3. 2022The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territorycited by 0
  4. 2026RespondeoQA: a Benchmark for Bilingual Latin-English Question Answeringcited by 0