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

arXiv: https://arxiv.org/abs/2202.02398 · HTML: https://mlsanity.com/paper/2202.02398/

## 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 (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://mlsanity.com/paper/2010.11856/) (2020)
- [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://mlsanity.com/paper/1711.05073/) (2017)
- [FQuAD: French Question Answering Dataset](https://mlsanity.com/paper/2002.06071/) (2020)
- [GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval](https://mlsanity.com/paper/2104.12741/) (2021)
- [Making Neural QA as Simple as Possible but not Simpler](https://mlsanity.com/paper/1703.04816/) (2017)
- [Dataset for the First Evaluation on Chinese Machine Reading Comprehension](https://mlsanity.com/paper/1709.08299/) (2017)
- [SelQA: A New Benchmark for Selection-based Question Answering](https://mlsanity.com/paper/1606.08513/) (2016)
- [MilkQA: a Dataset of Consumer Questions for the Task of Answer Selection](https://mlsanity.com/paper/1801.03460/) (2018)

## Built on by (most cited first, 4 of 4)

- [Benchmarks for Pir'a 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change](https://mlsanity.com/paper/2309.10945/) (2023)
- [Tracking environmental policy changes in the Brazilian Federal Official Gazette](https://mlsanity.com/paper/2202.10221/) (2022)
- [The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory](https://mlsanity.com/paper/2209.07928/) (2022)
- [RespondeoQA: a Benchmark for Bilingual Latin-English Question Answering](https://mlsanity.com/paper/2604.20738/) (2026)

## 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.