Developing Enterprise Chatbots : Learning Linguistic Structures

A chatbot is expected to be capable of supporting a cohesive and coherent conversation and be knowledgeable, which makes it one of the most complex intelligent systems being designed nowadays. Designers have to learn to combine intuitive, explainable language understanding and reasoning approaches with high-performance statistical and deep learning technologies. Today, there are two popular paradigms for chatbot construction: 1. Build a bot platform with universal NLP and ML capabilities so that a bot developer for a particular enterprise, not being an expert, can populate it with training data; 2. Accumulate a huge set of training dialogue data, feed it to a deep learning network and expect the trained chatbot to automatically learn "how to chat". Although these two approaches are reported to imitate some intelligent dialogues, both of them are unsuitable for enterprise chatbots, being unreliable and too brittle. The latter approach is based on a belief that some learning miracle will happen and a chatbot will start functioning without a thorough feature and domain engineering by an expert and interpretable dialogue management algorithms. Enterprise high-performance chatbots with extensive domain knowledge require a mix of statistical, inductive, deep machine learning and learning from the web, syntactic, semantic and discourse NLP, ontology-based reasoning and a state machine to control a dialogue. This book will provide a comprehensive source of algorithms and architectures for building chatbots for various domains based on the recent trends in computational linguistics and machine learning. The foci of this book are applications of discourse analysis in text relevant assessment, dialogue management and content generation, which help to overcome the limitations of platform-based and data driven-based approaches. Supplementary material and code is available at https://github.com/bgalitsky/relevance-based-on-parse-trees. -- Provided by publisher Front Matter ....Pages i-xv Introduction (Boris Galitsky)....Pages 1-11 Chatbot Components and Architectures (Boris Galitsky)....Pages 13-51 Explainable Machine Learning for Chatbots (Boris Galitsky, Saveli Goldberg)....Pages 53-83 Developing Conversational Natural Language Interface to a Database (Boris Galitsky)....Pages 85-120 Assuring Chatbot Relevance at Syntactic Level (Boris Galitsky)....Pages 121-162 Semantic Skeleton Thesauri for Question Answering Bots (Boris Galitsky)....Pages 163-175 Learning Discourse-Level Structures for Question Answering (Boris Galitsky)....Pages 177-219 Building Chatbot Thesaurus (Boris Galitsky)....Pages 221-252 A Content Management System for Chatbots (Boris Galitsky)....Pages 253-326 Rhetorical Agreement: Maintaining Cohesive Conversations (Boris Galitsky)....Pages 327-363 Discourse-Level Dialogue Management (Boris Galitsky)....Pages 365-426 A Social Promotion Chatbot (Boris Galitsky)....Pages 427-463 Enabling a Bot with Understanding Argumentation and Providing Arguments (Boris Galitsky)....Pages 465-532 Rhetorical Map of an Answer (Boris Galitsky)....Pages 533-555 Conclusions (Boris Galitsky)....Pages 557-559