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Found 3 papers, 3 papers with code
Consistency Training by Synthetic Question Generation for Conversational Question Answering
In our novel model-agnostic approach, referred to as CoTaH (Consistency-Trained augmented History), we augment the historical information with synthetic questions and subsequently employ consistency training to train a model that utilizes both real and augmented historical data to implicitly make the reasoning robust to irrelevant history.
PCoQA: Persian Conversational Question Answering Dataset
Our models include baseline models and pre-trained models, which are leveraged to boost the performance of the model.
KhabarChin: Automatic Detection of Important News in the Persian Language
This paper introduces the detection of important news, in a previously unexplored area, and presents a new benchmarking dataset (Khabarchin) for detecting important news in the Persian language.
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