Libro Deep Learning for Dialogue Systems: Chit-Chat and Beyond (Foundations and Trends(R) in Information R De Rui Yan; Juntao Li; Zhou Yu

portada Deep Learning for Dialogue Systems: Chit-Chat and Beyond (Foundations and Trends(R) in Information Retrieval)

Formato

Libro Físico

Editorial

Now Publishers

Autor

Rui Yan; Juntao Li; Zhou Yu

Idioma

Inglés

N° páginas

188

Encuadernación

Tapa Blanda

ISBN13

9781638280224

Deep Learning for Dialogue Systems: Chit-Chat and Beyond (Foundations and Trends(R) in Information Retrieval)

Rui Yan; Juntao Li; Zhou Yu (Autor) · Now Publishers · Tapa Blanda

Sin Stock

Reseña del libro "Deep Learning for Dialogue Systems: Chit-Chat and Beyond (Foundations and Trends(R) in Information Retrieval)"

With the rapid progress of deep neural models and the explosion of data resources, dialogue systems that supports extensive topics and chit-chat conversations are emerging in natural language processing (NLP), information retrieval (IR), and machine learning (ML). To facilitate the development of both retrieval-based chit-chat systems and IR tasks supported by them, the authors survey chit-chat systems from two perspectives: (1) techniques to build chit-chat systems, and (2) chit-chat components in completing IR tasks. The main contributions of this survey are: surveying the deep neural models; connecting the recently resurgent chit-chat systems and task-oriented system; introducing various solutions for challenges from different perspectives, including dataside and model-side solutions and utilization of extra resources; presenting data resources and evaluation methods for building retrieval-based and generation-based chit-chat systems. The authors also analyze the main challenges, possible new exploration directions and rising trends, which will shed light on building human-like systems. This survey is intended to bridge the researchers of IR and the NLP community to move chit-chat systems forward and support more IR tasks. It will be of interest to IR or NLP researchers who want to study chit-chat from different perspectives, IR researchers who need to complete their tasks with the assistance of chit-chat systems, engineers with hands-on experience in building these systems to leverage advanced chit-chat modeling techniques, or anyone who wants keep up with the frontier of chit-chat systems or learn how to build them with deep neural architectures.