bert qa ace 2005

BERT (Bidirectional Encoder Representations from Transformers) is a powerful language model developed by Google that can be fine-tuned for various natural language processing tasks, including question answering. The ACE 2005 dataset is a benchmark dataset for evaluating the performance of question answering systems in the context of information extraction and text analytics.

To apply BERT for question answering on the ACE 2005 dataset, one can fine-tune the model on a subset of the dataset using a task-specific objective function, such as the cross-entropy loss. During fine-tuning, BERT learns to map the input question and the relevant context passage to a probability distribution over all possible answers.

After fine-tuning, the BERT model can be evaluated on the test set of the ACE 2005 dataset using metrics such as precision, recall, and F1 score. The performance of the BERT model can be compared with other state-of-the-art question answering systems on the same dataset.