[3J1-GS-6a] 言語メディア処理：基礎

[3J1-GS-6a-02] A Study on Factors that Improve the Accuracy of Common Sense Reasoning in Natural Language Processing

Through the Linguistic Factors in Recent Benchmark Tasks

〇Hiromitsu Ota¹ (1. The Open University of Japan)

Keywords:knowledge representation, commonsense reasonoing, Natural language processing , pragmatics, knowledge base

In recent years, the development of Transfomer-derived models, mainly BERT and RoBERTa, has been remarkable, and they have been put to practical use in all fields of natural language processing such as machine translation, automatic summarization, and automatic sentence generation. Knowledge representation and reasoning are used to support these, and by incorporating general knowledge into machines such as robots, there are active movements aimed at improving the accuracy of information retrieval and question answering. In this study, while the movement centered on BERT is being established, it is assumed that the improvement of the corpus provides the intrinsic value, and from the linguistic aspect, which factor contributes to the improvement of accuracy, and on the other hand, it is insufficient. It is to consider whether it is done. In particular, the area of common sense reasoning is centered on international benchmarking tasks, but it is always criticized that the language model is limited because it is created with a limited distribution of data sets. .. In this, it is necessary to check the contents of the leaderboard of each task. Wikipedia, ConceptNet, etc. are expected to improve accuracy by common sense reasoning of written words, but they also linguistically propose how to integrate common sense reasoning into interactive spoken language dialogue.