Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System
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
- Editors:
- Srinivas Bangalore, Jennifer Chu-Carroll, Yunyao Li
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 137–144
- Language:
- URL:
- https://aclanthology.org/N18-3017
- DOI:
- 10.18653/v1/N18-3017
- Cite (ACL):
- Judith Gaspers, Penny Karanasou, and Rajen Chatterjee. 2018. Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pages 137–144, New Orleans - Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (Gaspers et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/N18-3017.pdf