• Smart Media Journal
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• v.11 no.10
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• pp.89-96
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• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.87-92
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• 2019

본 논문에서는 기계 독해를 이용한 웹 기반 오픈 도메인 한국어 질의응답 시스템에 대하여 서술한다. 하나의 사용자 질의에 대하여, 본 논문에서 제안된 시스템은 기 존재하는 검색 엔진을 이용하여 실시간으로 최대 1,500 개의 문서를 기계 독해 방식으로 분석하고, 각 문서별로 얻어진 답을 종합함으로써 최종 답변을 도출한다. 실험 결과, 제안된 시스템은 평균적으로 2초 이내의 실행 시간을 보였으며, 사람과 비교하여 86%의 성능을 나타내었다. 본 논문에서 제안된 시스템의 데모는 http://nlp-api.kakao.com에서 확인 가능하다.