Paper Info
Title
Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation.
Abstract
With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent. Since these models require large amounts of data and in-domain knowledge, expanding an equivalent service into new markets is disrupted by language barriers that inhibit dialog automation. This paper presents a user study to evaluate the utility of out-of-the-box machine translation technology to (1) rapidly bootstrap multilingual spoken dialog systems and (2) enable existing human analysts to understand foreign language utterances. We additionally evaluate the utility of machine translation in human assisted environments, where a portion of the traffic is processed by analysts. In English-Spanish experiments, we observe a high potential for dialog automation, as well as the potential for human analysts to process foreign language utterances with high accuracy.
Year
Venue
Field
2018
arXiv: Computation and Language
Language barrier,Dialog box,Spoken dialog systems,Computer science,Bootstrapping,Machine translation,Automation,Human–computer interaction,Natural language,Foreign language
DocType
Volume
Citations 
Journal
abs/1805.04453
0
PageRank 
References 
Authors
0.34
2
3
Name
Order
Citations
PageRank
Nick Ruiz1635.23
Srinivas Bangalore21319157.37
john chen319726.31