SelQA: A New Benchmark for Selection-based Question Answering

TL;DR

SelQA introduces a new, challenging benchmark dataset for selection-based question answering, emphasizing diversity and reduced word overlap to improve system evaluation and development.

Contribution

The paper presents SelQA, a novel dataset with an annotation scheme that enhances diversity and reduces word co-occurrence, facilitating better training and evaluation of QA systems.

Findings

Strong baseline results established for answer sentence selection.

Effective crowdsourcing annotation scheme developed.

Dataset covers ten prevalent Wikipedia topics.

Abstract

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.