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

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.

[1]  Haizhou Li,et al.  A bootstrapping approach for SLU portability to a new language by inducting unannotated user queries , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[3]  Adam L. Berger,et al.  A Maximum Entropy Approach to Natural Language Processing , 1996, CL.

[4]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[5]  Fabrice Lefèvre,et al.  Combination of stochastic understanding and machine translation systems for language portability of dialogue systems , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Philipp Cimiano,et al.  Instance Selection Improves Cross-Lingual Model Training for Fine-Grained Sentiment Analysis , 2015, CoNLL.

[7]  Santanu Pal,et al.  Multi-source Neural Automatic Post-Editing: FBK’s participation in the WMT 2017 APE shared task , 2017, WMT.

[8]  Frédéric Béchet,et al.  On the use of machine translation for spoken language understanding portability , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Fabrice Lefèvre,et al.  Cross-lingual spoken language understanding from unaligned data using discriminative classification models and machine translation , 2010, INTERSPEECH.

[10]  Encarna Segarra,et al.  Combining multiple translation systems for Spoken Language Understanding portability , 2012, 2012 IEEE Spoken Language Technology Workshop (SLT).

[11]  Stefan Riezler,et al.  A Shared Task on Bandit Learning for Machine Translation , 2017, WMT.