A Dataset of Open-Domain Question Answering with Multiple-Span Answers

TL;DR

CLEAN is a new Chinese multi-span question answering dataset that covers diverse open-domain topics and includes many questions requiring descriptive answers, addressing previous limitations in available benchmarks.

Contribution

The paper introduces CLEAN, a comprehensive Chinese MSQA dataset with diverse questions and descriptive answers, along with baseline models and analysis.

Findings

CLEAN presents diverse open-domain questions with many requiring descriptive answers.

Baseline models reveal the dataset's complexity and challenge.

CLEAN is publicly available for research use.

Abstract

Multi-span answer extraction, also known as the task of multi-span question answering (MSQA), is critical for real-world applications, as it requires extracting multiple pieces of information from a text to answer complex questions. Despite the active studies and rapid progress in English MSQA research, there is a notable lack of publicly available MSQA benchmark in Chinese. Previous efforts for constructing MSQA datasets predominantly emphasized entity-centric contextualization, resulting in a bias towards collecting factoid questions and potentially overlooking questions requiring more detailed descriptive responses. To overcome these limitations, we present CLEAN, a comprehensive Chinese multi-span question answering dataset that involves a wide range of open-domain subjects with a substantial number of instances requiring descriptive answers. Additionally, we provide established models from relevant literature as baselines for CLEAN. Experimental results and analysis show the characteristics and challenge of the newly proposed CLEAN dataset for the community. Our dataset, CLEAN, will be publicly released at zhiyiluo.site/misc/clean_v1.0_ sample.json.