Center • Past experience on • Cyber security and defense industry • Smartphone industry • Familiar with • Machine learning • Natural language processing • Software development • Cloud native architecture design
in 2018 • AI group is composed of data scientists and software developers • Our mission is to realize AI-based solution in banking scenario • We currently focus on • Computer Vision (CV) • Natural Language Processing (NLP) Retrieved from https://www.ithome.com.tw/news/131697
POS tagging (ckiptagger) • Feature extraction • Text and POS tags within context Model I : CRF for Word-Level Feature 現在美金一年期定存是多少 Text 現在(Nd) 美金(Na) 一年期(Na) 定存(Na) 是(SHI) 多少(Neqa) Tokens …, ( -1:現在, -1:Nd, 0:美金, 0:Na, 1:一年期, 1:NA ), … Feature vector Context windows: 3 tokens • Model • Conditional Random Field (CRF) (scikit-learn) Feature engineering Model Training
II : Bi-LSTM-CRF for Word-Level Embedding 現在美金一年期定存是多少 Text 現在 美金 一年期 定存 是 多少 Tokens • Model • Embedding Layer (keras) • Long Short-Term Memory (LSTM) layer (keras) • CRF layer (keras) Embedding learning Features learning Model training
Data • Preserve privacy • Do not hand data over to big tech company • Transparency • Community support • Task-oriented dialogue architecture • Customizable components Rasa characteristics CTBC strategy • Customize Mandarin- based component • Integration on core technology • Compliance on Security and Regulation • Customized scenario • Ownership on core technology
systems • Intent recognizer and entity extractor are key components to realize NLU by machine learning techniques and annotated data • DNN performs generally better than traditional method but not for all tasks • Rasa powered by open source offers a framework for conversational assistant development from scratch Summary