This paper presents a comparison of BERT-based question answering models on the SQuAD and Natural Questions datasets, highlighting the strengths and weaknesses of each approach.
In this article, we compare the performance of BERT on various question answering datasets, including SQuAD, TriviaQA, and HotpotQA, and discuss the implications for real-world applications.
This webpage provides an overview of popular question answering datasets, including SQuAD, Natural Questions, and TriviaQA, and offers a platform for exploring and comparing the performance of different models, including BERT.
This technical report from MIT evaluates the performance of BERT-based question answering systems on a range of datasets, including SQuAD and Natural Questions, and discusses the challenges of developing robust and generalizable models.
In this video tutorial, we walk through the process of using BERT for question answering, including data preparation, model training, and evaluation on the SQuAD dataset.
The Stanford Question Answering Dataset (SQuAD) is a popular benchmark for evaluating question answering models, including BERT, and consists of over 100,000 questions posed by crowdworkers on a set of Wikipedia articles.
This survey article provides a comprehensive overview of the use of BERT for question answering, including a comparison of different datasets and models, and discusses the current challenges and future directions in the field.
The Natural Questions dataset is a benchmark for question answering that consists of real-user questions and answers, and is designed to evaluate the ability of models, including BERT, to answer complex and open-ended questions.