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Found 5 papers, 2 papers with code
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers.
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types.
Do Answers to Boolean Questions Need Explanations? Yes
Existing datasets that contain boolean questions, such as BoolQ and TYDI QA , provide the user with a YES/NO response to the question.
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
The TechQA Dataset
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain.
