Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System

Judith Gaspers, Penny Karanasou, Rajen Chatterjee


Abstract
This paper investigates the use of Machine Translation (MT) to bootstrap a Natural Language Understanding (NLU) system for a new language for the use case of a large-scale voice-controlled device. The goal is to decrease the cost and time needed to get an annotated corpus for the new language, while still having a large enough coverage of user requests. Different methods of filtering MT data in order to keep utterances that improve NLU performance and language-specific post-processing methods are investigated. These methods are tested in a large-scale NLU task with translating around 10 millions training utterances from English to German. The results show a large improvement for using MT data over a grammar-based and over an in-house data collection baseline, while reducing the manual effort greatly. Both filtering and post-processing approaches improve results further.
Anthology ID:
N18-3017
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)
Month:
June
Year:
2018
Address:
New Orleans - Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–144
Language:
URL:
https://www.aclweb.org/anthology/N18-3017
DOI:
10.18653/v1/N18-3017
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-3017.pdf
Video:
 http://vimeo.com/277669655