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Pretrained Transformers for Simple Question Answering over Knowledge Graphs
[article]
2020
arXiv
pre-print
Answering simple questions over knowledge graphs is a well-studied problem in question answering. Previous approaches for this task built on recurrent and convolutional neural network based architectures that use pretrained word embeddings. It was recently shown that finetuning pretrained transformer networks (e.g. BERT) can outperform previous approaches on various natural language processing tasks. In this work, we investigate how well BERT performs on SimpleQuestions and provide an
arXiv:2001.11985v1
fatcat:r5dz2n47bzefrd5skjkimgwtym