Natural Language to Structured Query Generation via Meta-Learning Po-Sen Huang 1 Chenglong Wang 2 Rishabh Singh 3 * Wen-tau Yih 4 Xiaodong He 5 * 1. Introduction Conventional supervised training is a pervasive paradigm

Source URL: uclmr.github.io

• Machine learning
• Learning
• Artificial intelligence
• Meta learning
• Support vector machine
• Supervised learning
• Training
• test
• and validation sets

Natural Language to Structured Query Generation via Meta-Learning Po-Sen Huang? , Chenglong Wang† , Rishabh Singh? , Wen-tau Yih‡ , Xiaodong He∗ ? Microsoft Research, † University of Washington, ‡ Allen Instit

Source URL: rishabhmit.bitbucket.io

• Machine learning
• Computing
• Data management
• Artificial intelligence
• Computational neuroscience
• Artificial neural networks
• Computational linguistics
• Meta learning
• Supervised learning
• Support vector machine
• Deep learning
• SQL syntax

An Investigation into the Validity of Some Metrics for Automatically Evaluating Natural Language Generation Systems Ehud Reiter∗ University of Aberdeen

Source URL: homepages.abdn.ac.uk

An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation Stefan Constantin, Jan Niehues, and Alex Waibel Abstract Recently advancements in deep learning allowed the development of

Source URL: www.colips.org

Jens Knoop, Uwe Zdun (Hrsg.): Software Engineering 2016, Lecture Notes in Informatics (LNI), Gesellschaft f¨ur Informatik, BonnSupporting Process Model Validation through Natural Language Generation Henrik Leop

Source URL: subs.emis.de

Using Natural Language Generation Technology to Improve Information Flows in Intensive Care Units Jim Hunter, Albert Gatt, François Portet, Ehud Reiter and Somayajulu Sripada1 Abstract. In the drive to improve patient s

Source URL: homepages.abdn.ac.uk

Chapter 4 Generating Texts in Different Styles Ehud Reiter1 and Sandra Williams2 Abstract Natural Language Generation (nlg) systems generate texts in

Source URL: homepages.abdn.ac.uk

An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation Stefan Constantin, Jan Niehues, and Alex Waibel Institute for Anthropomatics and Robotics, Informatics, Interactive Systems

Source URL: www.colips.org

Journal of Artificial Intelligence Research516 Submitted 11/02; published 6/03 Acquiring Correct Knowledge for Natural Language Generation

Source URL: homepages.abdn.ac.uk

AN OVERVIEW OF SHALLOW XML-BASED NATURAL LANGUAGE GENERATION Graham Wilcock University of Helsinki Abstract

Source URL: www.ling.helsinki.fi