Research notes
Chatbotdialogue management is designed to summarize the current dialogue state and determine what the system should do. It is typically divided into two sub-modules: “Dialogue State Tracking,” which is responsible for updating the dialogue state, taking as inputthe Natural Language Understanding modulederived…
Read article: [Conversational AI-3] Chatbot Memory and Decision-Making – Dialogue ManagementAlthough Computer Vision has made significant progress through Deep Learning technology, the adoption of Deep Learning in the field of Natural Language Processing is still in its early stages of development. Regarding Chatbots, since the Turing Test was passed by the chatbot…
Read article: [Conversational AI-4] Challenges and Development Trends of ChatbotsPre-trained language models can be used for tasks in Natural Language Understanding such as Named Entity Recognition, Extraction-based Question Answering, Sentiment analysis, Document Classification, and Natural Language Inference. As well as tasks in Natural Language Generation such as Machine translation, Automatic summarization,…
Read article: [Conversational AI-7] Comparison of Pre-trained Language Models (ELMO, BERT, GPT-2)This article uses the company where the author works as an example; without disclosing company confidential information, it introduces the ‘relevant departments, development processes, and job responsibilities’ involved in AI product R&D, unveiling the mystery behind the AI industry. The company’s main…
Read article: [Conversational AI-8] Introduction to R&D Departments and Development ProcessesTowards the end of the year, due to work tasks, I surveyed several papers on Recommender Systems (hereinafter referred to as RS). When I was writing my master’s thesis, Deep Learning (hereinafter referred to as DL) was not yet popular. In the…
Read article: A Survey of Deep Learning-Based Recommendation Algorithms
