PCoQA: Persian Conversational Question Answering Dataset

Humans seek information regarding a specific topic through performing a conversation containing a series of questions and answers. In the pursuit of conversational question <PRE_TAG>answering</POST_TAG> research, we introduce the PCoQA, the first Persian Conversational Question Answering dataset, a resource comprising information-seeking dialogs encompassing a total of 9,026 contextually-driven questions. Each dialog involves a questioner, a responder, and a document from the Wikipedia; The questioner asks several inter-connected questions from the text and the responder provides a span of the document as the answer for each question. PCoQA is designed to present novel challenges compared to previous question answering datasets including having more open-ended non-factual <PRE_TAG>answers</POST_TAG>, longer <PRE_TAG>answers</POST_TAG>, and fewer lexical overlaps. This paper not only presents the comprehensive PCoQA dataset but also reports the performance of various benchmark models. Our models include baseline models and pre-trained models, which are leveraged to boost the performance of the model. The dataset and benchmarks are available at our Github page.