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

arXiv: https://arxiv.org/abs/2109.08627 · HTML: https://mlsanity.com/paper/2109.08627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Are Sixteen Heads Really Better than One?](https://mlsanity.com/paper/1905.10650/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Patient Knowledge Distillation for BERT Model Compression](https://mlsanity.com/paper/1908.09355/) (2019)
- [Revealing the Dark Secrets of BERT](https://mlsanity.com/paper/1908.08593/) (2019)
- [Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning](https://mlsanity.com/paper/2002.08307/) (2020)
- [Distilling Task-Specific Knowledge from BERT into Simple Neural Networks](https://mlsanity.com/paper/1903.12136/) (2019)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [BERT-of-Theseus: Compressing BERT by Progressive Module Replacing](https://mlsanity.com/paper/2002.02925/) (2020)
- [Knowledge Distillation from Internal Representations](https://mlsanity.com/paper/1910.03723/) (2019)
- [TransQuest: Translation Quality Estimation with Cross-lingual Transformers](https://mlsanity.com/paper/2011.01536/) (2020)
- [MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset](https://mlsanity.com/paper/2010.04480/) (2020)
- [LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression](https://mlsanity.com/paper/2004.04124/) (2020)
- [Small and Practical BERT Models for Sequence Labeling](https://mlsanity.com/paper/1909.00100/) (2019)
- [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://mlsanity.com/paper/2004.05686/) (2020)
- [TransQuest at WMT2020: Sentence-Level Direct Assessment](https://mlsanity.com/paper/2010.05318/) (2020)
- [Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data](https://mlsanity.com/paper/2105.15071/) (2021)
- [Quality Estimation without Human-labeled Data](https://mlsanity.com/paper/2102.04020/) (2021)
- [Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task](https://mlsanity.com/paper/1907.10352/) (2019)
- [Practical Perspectives on Quality Estimation for Machine Translation](https://mlsanity.com/paper/2005.03519/) (2020)

## Abstract (arXiv)

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 make them infeasible for 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.