"A dialogue system, or conversational agent (CA), is a computer system intended to converse with a human."
The design and implementation of systems that can converse with humans.
Natural Language Processing (NLP): The study of processing natural language text or speech in unstructured and ambiguous formats to determine its meaning and intent.
Machine Learning (ML): The practice of training algorithms to learn from data and perform specific tasks, often used in dialogue systems to enhance their accuracy and effectiveness.
Speech Recognition: The ability of machines to recognize and interpret spoken language, often used in voice-activated systems and dialogue interfaces.
Text-to-Speech (TTS): The conversion of written or typed text into spoken words, also commonly used in dialogue systems to communicate with users.
Natural Language Generation (NLG): The creation of natural language text or speech from data or machine-generated language, often used in chatbots and conversation engines.
Intent Classification: The process of identifying a user's intent or purpose behind a specific statement or response in order to generate an appropriate dialogue response.
Named Entity Recognition (NER): The identification and categorization of specific named entities within text or speech, such as people, places, organizations, and dates.
Machine Translation (MT): The translation of text from one language to another using computerized systems that apply NLP and ML techniques.
Sentiment Analysis: The analysis of text or speech to determine the emotional tone or attitude of the speaker, often used in dialogue systems to tailor responses.
Chatbot Design and Development: The principles and methodologies involved in designing and developing effective chatbots and conversational interfaces.
User Experience Design (UX): The process of designing services or products that optimize user experiences, including user feedback and interaction design.
Ethics in Dialogue Systems: The ethical implications of integrating artificial intelligence, machine learning, and natural language into dialogue systems, including privacy, bias, and fairness.
Dialogue System Evaluation: Assessing and testing the effectiveness and accuracy of dialogue systems, using metrics such as user satisfaction, task completion rate, and dialogue quality.
Human-Computer Interaction (HCI): The practice and study of designing and evaluating how people interact with technology, including dialogue systems.
Semantics: The branch of linguistics concerning the meaning of words and phrases, as well as their relationship to one another. Often leveraged to facilitate natural language processing and understanding in dialogue systems.
Ontology: A structure defining and organizing information used to drive dialogue systems. Often includes data about entities, concepts, and relationships, that can help support precise and structured understanding of input from users.
Contextual Approaches: A variety of strategies and methods used to employ contextual information to improve the effectiveness of dialogue systems. Includes techniques such as memory, attention, and reinforcement learning.
Knowledge Graphs: A method used to represent knowledge and information in a structured format that helps effectively process and present data in dialogue systems.
Multi-modal dialogue systems: Dialogue systems that integrate multiple types of communication such as text, speech, images, and other forms of sensory communication. This can help facilitate more natural and efficient interaction with the user.
User Modeling: The process of continually assessing and adapting dialogue systems to fit user preferences and behavior patterns. Often involves Machine Learning algorithms that improve over time as the system interacts with individual users.
Rule-based Dialogue Systems: These Dialogue Systems are based on predefined rules and static scripts. They work based on the set of rules that must be followed, like a decision tree.
Frame-based Dialogue Systems: These systems incorporate a knowledge model of the domain to structure the conversation. Frames are used to capture the essential elements of the domain, including objects and their attributes, events and their participants.
Statistical Dialogue Systems: These Dialogue Systems work by using machine learning techniques to generate responses from statistical models trained on a lot of data. They are based on complex natural language processing and use machine learning algorithms like deep learning and neural networks to develop the best possible response.
Retrieval-based Dialogue Systems: Retrieval-based Dialogue Systems fetch an answer from a database of pre-defined responses based on the keywords specified in the user's query.
Generative Dialogue Systems: Unlike retrieval-based models, Generative Dialogue Systems generate their responses organically, similar to a human's creative process, based on a given input. They generally use complex deep learning algorithms to create contextual responses.
Hybrid Dialogue Systems: As the name suggests, Hybrid Dialogue Systems combine two or more types of Dialogue Systems to build an optimized communication exchange. The most common hybrid combinations are rule-based and statistical learning systems or retrieval-based and generative systems.
Multimodal Dialogue Systems: These Dialogue Systems combine text, speech, and visual input form various modalities and correlate them for communication.
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"The elements of a dialogue system are not defined because this idea is under research, however, they are different from chatbot."
"The typical GUI wizard engages in a sort of dialogue."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"A dialogue system, or conversational agent (CA), is a computer system intended to converse with a human."
"The elements of a dialogue system are not defined because this idea is under research."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"The dialogue state is trivial [in a typical GUI wizard]."
"...they are different from chatbot."
"A dialogue system... is a computer system intended to converse with a human."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"The dialogue state is trivial [in a typical GUI wizard]."
"The elements of a dialogue system are not defined because this idea is under research."
"The elements of a dialogue system are not defined because this idea is under research, however, they are different from chatbot."
"Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel."
"A dialogue system, or conversational agent (CA), is a computer system intended to converse with a human."
"The typical GUI wizard engages in a sort of dialogue, but it includes very few of the common dialogue system components, and the dialogue state is trivial."