Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications

Conference on Empirical Methods in Natural Language Processing (EMNLP)


Sentence-level Quality Estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and thus infeasible for many real-world applications. In this work, we evaluate several model compression techniques for QE and find that, despite their popularity in other NLP tasks, they lead to poor performance in this regression setting. We observe that a full model parameterization is required to achieve SoTA results in a regression task. However, we argue that the level of expressiveness of a model in a continuous range is unnecessary given the downstream applications of QE, and show that reframing QE as a classification problem and evaluating QE models using classification metrics would better reflect their actual performance in real-world applications.

Related Publications

All Publications

NeurIPS - December 5, 2021

Interpretable agent communication from scratch (with a generic visual processor emerging on the side)

Roberto Dessì, Eugene Kharitonov, Marco Baroni

Electronics (MDPI) Journal - November 4, 2021

Performance Evaluation of Offline Speech Recognition on Edge Devices

Santosh Gondi, Vineel Pratap

EMNLP Conference on Machine Translation (WMT) - October 1, 2020

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia

Electronics (MDPI) Journal - November 10, 2021

Performance and Efficiency Evaluation of ASR Inference on the Edge

Santosh Gondi, Vineel Pratap

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookie Policy