PCoQA: Persian Conversational Question Answering Dataset
Hamed Hematian Hemati Atousa Toghyani Atena Souri Sayed Hesam Alavian Hossein Sameti Hamid Beigy
Humans seek information regarding a specific topic through performing a
conversation containing a series of questions and answers. In the pursuit of
conversational question answering research, we introduce the PCoQA, the first
\textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering
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 answers, longer answers, 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.