應用深度學習於事實型問答系統之研究

A Study of Deep Learning for Question Answering System in Factoid Question

郭育菱(Yu-Ling Kuo)

指導教授 ： 戴敏育

淡江大學/商管學院/大數據分析與商業智慧碩士學位學程/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00839

摘要

近年來自然語言處理已經成為電腦科學領域與人工智慧領域中的一個重要研究方向，其中一項是回答針對文章所提出問題的機器閱讀理解。加上語音助理的崛起，使問答系統更能應用在商業運作。過去文獻中較少結合預訓練模型與判定事實型問句與答案的分類是否相同，並完成問答系統。
本研究利用系統發展研究方法論建構一個問答系統。使用台達閱讀理解資料集建立一個模型，實現基於事實型問答的系統，並結合比對問答句的分類以EM與F1進行評估。最後比對是否能夠增加EM的比例，比較預期答案類型是否能有效增加回答正確率。
經研究證實透過比對問答句的分類可以提高EM比例，在開發集與測試集分別提高3.25%與3.69%。本研究的研究貢獻為建構建構出了一個利用BERT預訓練模型應用在DRCD資料集，並加入預期答案分析比較的問答系統。並證實在問句與答案分類相同情況下，會提高問答系統的預測正確率。

關鍵字

人工智慧 ； 自然語言處理 ； 深度學習 ； BERT ； 事實型問答 ； 問答系統

並列摘要

In recent years, natural language processing has become an important research direction in the field of computer science and artificial intelligence. One of them is machine reading comprehension. In the past, the literature rarely combined the pre-train model and whether the classification of factoid questions and answers is the same, and completed a question answering system. This research uses Delta's reading comprehension dataset to build a model to implement a factoid question answering system, and combine the classification of question and answer sentences to evaluate with EM and F1. It has been confirmed by research that the classification of question and answer sentences can increase the percentage of EM, which is increased by 3.25% and 3.69% in the development and test datasets, respectively. The research contribution of this research is to construct a question answering system that uses the BERT pre-training model to apply to the DRCD dataset, and adds the expected answer analysis and comparison. And confirmed that the question and answer classification is the same, it will improve the EM score of the question answering system.

並列關鍵字

Artificial Intelligence ； Natural Language Processing ； Deep Learning ； BERT ； Factoid Question Answering ； Question Answering

參考文獻

參考文獻

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