Attending knowledge facts with BERT-like models in Question-Answering : disappointing results and
Guillaume Le Berre (University of Montreal) and Philippe Langlais (University of Montreal).
Since the first appearance of BERT, pretrained BERT inspired models (XLNet, Roberta, ...) have delivered state-of-the-art results in a large number of Natural Language Processing tasks. This includes question-answering where previous models performed relatively poorly particularly on datasets with a limited amount of data. In this paper we perform experiments with BERT on two such datasets that are OpenBookQA and ARC. Our aim is to understand why, in our experiments, using BERT sentence representations inside an attention mechanism on a set of facts tends to give poor results. We demonstrate that in some cases, the sentence representations proposed by BERT are limited in terms of semantic and that BERT often answers the questions in a meaningless way.