This paper introduces BERT, a pre-trained language model that achieves state-of-the-art results on a wide range of natural language processing tasks, including question answering.
This article analyzes BERT's performance on various question answering datasets, including SQuAD and TriviaQA, and discusses the implications of its results.
This tutorial provides an overview of using BERT for question answering tasks, including fine-tuning the model and evaluating its performance on benchmark datasets.
This dataset provides a large collection of questions and answers for evaluating machine comprehension systems, including BERT, and has become a standard benchmark for question answering research.
This paper presents a critical evaluation of BERT's performance on question answering datasets, highlighting both its strengths and weaknesses, and discussing the limitations of current evaluation metrics.
This survey provides a comprehensive overview of the current state of BERT-based question answering systems, including their architecture, training methods, and performance on various datasets.
This video tutorial demonstrates how to fine-tune BERT for question answering tasks using the Hugging Face Transformers library and provides tips for improving its performance.
This tutorial provides a detailed introduction to using BERT for question answering tasks, including data preparation, model fine-tuning, and evaluation, and is designed for students and practitioners.