利用Seq2Seq機器學習於延伸機器人對答內容

Abstract

近年來深度學習蓬勃發展，讓機器學習方式學習到人類的行為模式，而在深度學習薰陶下，機器人對話又慢慢的浮出水面，在傳統機器人對話中以自己建立的資料庫來打造機器人，必須依賴於強大問答資料庫一個問題一個答案的方式進行，往往產生出資料所花費時間與理想超出許多，在面對客戶會想知道客戶問什麼樣的問題，產生出問題成為了重要關鍵，本研究利用seq2seq模型進行訓練打造一個產生問句模型，使機器人可以與人一樣產生問句，以利於發現客戶所會提的問題。

In recent years, deep learning has flourished, allowing machine learning to learn human behavior patterns. Under the deep learning, robot dialogue has slowly surfaced, and in the traditional robot dialogue, the robot is built with its own database. It must rely on the powerful question and answer database to answer one question and one answer. It often takes a lot of time and ideals to generate the information. In the face of the customer, they will want to know what kind of problem the customer asks, and the problem becomes an important key. This study uses the seq2seq model to train to create a question-making model that allows the robot to generate questions like people, in order to help identify problems that customers are asking.