Quality Estimation without Human-labeled Data

• URL: http://arxiv.org/abs/2102.04020v1
• Date: Mon, 8 Feb 2021 06:25:46 GMT
• Title: Quality Estimation without Human-labeled Data
• Authors: Yi-Lin Tuan, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Francisco Guzm\'an, Lucia Specia
• Abstract summary: Quality estimation aims to measure the quality of translated content without access to a reference translation. We propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data. We train off-the-shelf architectures for supervised quality estimation on our synthetic data and show that the resulting models achieve comparable performance to models trained on human-annotated data.
• Score: 25.25993509174361
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quality estimation aims to measure the quality of translated content without access to a reference translation. This is crucial for machine translation systems in real-world scenarios where high-quality translation is needed. While many approaches exist for quality estimation, they are based on supervised machine learning requiring costly human labelled data. As an alternative, we propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data. We train off-the-shelf architectures for supervised quality estimation on our synthetic data and show that the resulting models achieve comparable performance to models trained on human-annotated data, both for sentence and word-level prediction.

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