English

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

Computation and Language 2021-03-16 v1

Abstract

This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG). The dataset was created using a semi-automated framework for generating diverse paraphrasing of the answers using techniques such as back-translation. The existing datasets for conversational question answering over KGs (single-turn/multi-turn) focus on question paraphrasing and provide only up to one answer verbalization. However, ParaQA contains 5000 question-answer pairs with a minimum of two and a maximum of eight unique paraphrased responses for each question. We complement the dataset with baseline models and illustrate the advantage of having multiple paraphrased answers through commonly used metrics such as BLEU and METEOR. The ParaQA dataset is publicly available on a persistent URI for broader usage and adaptation in the research community.

Keywords

Cite

@article{arxiv.2103.07771,
  title  = {ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation},
  author = {Endri Kacupaj and Barshana Banerjee and Kuldeep Singh and Jens Lehmann},
  journal= {arXiv preprint arXiv:2103.07771},
  year   = {2021}
}

Comments

18th Extended Semantic Web Conference 2021 (ESWC'2021) - Resources Track

R2 v1 2026-06-24T00:06:44.256Z