Perplexity-Driven Case Encoding Needs Augmentation for CAPITALIZATION Robustness

Abstract

Subword segmentation methods are the predominant solution to vocab sparsity in NMT. However, they cannot currently handle capitalization well. We re-encode case to allow the perplexity-driven SPM unigram language model algorithm to learn how to segment capitalization. Since naturally occurring data accurately describes the prevalence of capi-talization but underestimates the importance humans ascribe to capitalization robustness, we propose data augmentation to fill this gap. We demonstrate that our proposed method improves translation quality on ALL CAPS, lower cased, and Title Case, while maintaining quality on standard test sets. In contrast to prior work, our proposed method has minimal impact on decoding speed. We release our code: github.com/marian-nmt/sentencepiece .