Conversational QA Dataset

Conversational QA Dataset是一个自动生成的对话式问答数据集，由韩国浦项科技大学人工智能研究生院创建。该数据集通过从输入文本中提取有价值的问题短语，并结合先前的对话历史来生成问答对。数据集的创建过程包括上下文答案提取和对话式问题生成两个阶段，旨在通过简单的答案修订方法显著提高合成数据的质量。该数据集主要应用于对话式问答系统的领域适应性研究，以解决特定领域内构建健壮问答系统所需的大规模数据集问题。Conversational QA Dataset is an automatically generated conversational question answering dataset created by the Graduate School of Artificial Intelligence at Pohang University of Science and Technology (POSTECH), South Korea. It generates question-answer pairs by extracting valuable question phrases from input texts and combining them with prior conversation history. The dataset creation process consists of two stages: context-based answer extraction and conversational question generation, aiming to significantly improve the quality of synthetic data through a simple answer revision method. This dataset is mainly applied in domain adaptation research for conversational question answering systems, to address the challenge of obtaining large-scale datasets required for building robust question answering systems in specific domains.

搜集汇总

背景与挑战

背景概述

Conversational QA Dataset是由韩国浦项科技大学人工智能研究生院自动生成的对话式问答数据集，通过从输入文本提取问题短语并结合对话历史生成问答对，包含上下文答案提取和对话式问题生成两个阶段，采用答案修订方法提升数据质量。该数据集专为对话式问答系统的领域适应性研究设计，旨在解决特定领域大规模数据集构建的难题。