DeepKLM - 통사 실험을 위한 전산 언어모델 라이브러리 -

Abstract

This paper introduces DeepKLM, a deep learning library for syntactic experiments. The library enables researchers to use the state-of-the-art deep computational language model, based on BERT (Bidirectional Encoder Representations from Transformers). The library, written in Python, works to fill the masked part of a sentence with a specific token, similar to the Cloze task in the traditional language experiments. The output value of surprisal is related to human language processing in terms of speed and complexity. The library additionally provides two visualization tools of the heatmap and the attention head visualization. This article also provides two case studies of NPIs and reflexives employing the library. The library has room for improvement in that the BERT-based components are not entirely on par with those in human language sentences. Despite such limits, the case studies imply that the library enables us to assess human and deep learning machines’ language ability.