Quality Estimation without Human-labeled Data

Autor:	Tuan, Yi-Lin, El-Kishky, Ahmed, Renduchintala, Adithya, Chaudhary, Vishrav, Guzmán, Francisco, Specia, Lucia
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by EACL2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2102.04020 Zobrazit plný text záznamu View this record from Arxiv