Developing Enterprise Chatbots: Learning Linguistic Structures
Authors: Boris Galitsky
ISBN-10: 3030042987
ISBN-13: 9783030042981
Edition 版次: 1st ed. 2019
Publication Date 出版日期: 2019-04-05
Print Length 页数: 559 pages
Book Description
By finelybook
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 github.com/bgalitsky/relevance-based-on-parse-trees
1.Introduction
2.Chatbot Components and Architectures
3.Explainable Machine Learning for Chatbots
4.Developing Conversational Natural Language Interface to a Database
5.Assuring Chatbot Relevance at Syntactic Level
6.Semantic Skeleton Thesauri for Question Answering Bots
7.Learning Discourse-Level Structures for Question Answering
8.Building Chatbot Thesaurus
9.A Content Management System for Chatbots
10.Rhetorical Agreement: Maintaining Cohesive Conversations
11.Discourse-Level Dialogue Management
12.A Social Promotion Chatbot
13.Enabling a Bot with Understanding Argumentation and Providing Arguments
14.Rhetorical Map of an Answer
15.Conclusions
