Jae-Ok Min, Jin-Woo Park, Yu-Jeong Jo, Bong-Gun Lee

KIPS Transactions on Software and Data Engineering, Vol. 9, No. 4, pp. 145-152, Apr. 2020
https://doi.org/10.3745/KTSDE.2020.9.4.145,   PDF Download:
Keywords: Natural Language Processing, MRC, Machine Reading Comprehension, Patent, BERT
Abstract

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

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