# SelQA: A New Benchmark for Selection-based Question Answering

arXiv: https://arxiv.org/abs/1606.08513 · HTML: https://mlsanity.com/paper/1606.08513/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2016.

## Built on by (most cited first, 6 of 6)

- [Double Retrieval and Ranking for Accurate Question Answering](https://mlsanity.com/paper/2201.05981/) (2022)
- [Pir'a: A Bilingual Portuguese-English Dataset for Question-Answering about the Ocean](https://mlsanity.com/paper/2202.02398/) (2022)
- [Indian Legal NLP Benchmarks : A Survey](https://mlsanity.com/paper/2107.06056/) (2021)
- [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)
- [Detecting Prompt Injection Attacks Against Application Using Classifiers](https://mlsanity.com/paper/2512.12583/) (2025)
- [Implications of Deep Circuits in Improving Quality of Quantum Question Answering](https://mlsanity.com/paper/2305.07374/) (2023)

## Abstract (arXiv)

This paper presents a new selection-based question answering dataset, SelQA. The dataset consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. We introduce a corpus annotation scheme that enhances the generation of large, diverse, and challenging datasets by explicitly aiming to reduce word co-occurrences between the question and answers. Our annotation scheme is composed of a series of crowdsourcing tasks with a view to more effectively utilize crowdsourcing in the creation of question answering datasets in various domains. Several systems are compared on the tasks of answer sentence selection and answer triggering, providing strong baseline results for future work to improve upon.