Building machine reading comprehension model from scratch

Abstract

In this paper, we introduce a machine reading comprehension model and how we built this model from scratch. Reading comprehension is a crucial requisite for artificial intelligence applications, such as QuestionAnswering systems, chatbots, virtual assistants etc. Reading comprehension task requires the highest complexity of natural language processing methods. In recent years, the transformer neural architecture could achieve the ability to solve high complexity tasks. To make these applications available in Hungarian as well it is inevitable to design a Hungarian corpus of reading comprehension so that the pretrained models can be fine-tuned on this dataset. In our research, we have created the HuRC (Hungarian Reading Comprehension) corpus, which is the first dataset in Hungarian aiming to train, test and evaluate language models on a reading comprehension task. We built such a dataset based on the English ReCoRD corpus. This is a dataset of 120,000 examples consisting of news articles containing a passage and a close-style query, where a named entity is masked and the reference answer has to be found in a list. Using the evaluated dataset and transformers’ question-answering library, we have built the first neural machine reading comprehension models in commonsense reasoning task for Hungarian.