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.

Stats

• citations3
• S2 citations—
• github stars0
• HF likes0
• heat score4.52
• arxiv keygaspers2018selecting

Related papers

• Language Model Bootstrapping Using Neural Machine Translation For Conversational Speech Recognition (2019)5.24
• Bootstrap An End-to-end ASR System By Multilingual Training, Transfer Learning, Text-to-text Mapping And Synthetic Audio (2020)5.24
• Harnessing Indirect Training Data For End-to-end Automatic Speech Translation: Tricks Of The Trade (2019)0.00
• Learning Domain Specific Language Models For Automatic Speech Recognition Through Machine Translation (2021)0.00
• Acquiring Pronunciation Knowledge From Transcribed Speech Audio Via Multi-task Learning (2024)0.00
• Generating Synthetic Speech From Spokenvocab For Speech Translation (2022)5.38
• Unsupervised Data Validation Methods For Efficient Model Training (2024)0.00
• Low-latency Neural Speech Translation (2018)9.03