Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic
  Sentence Segmentation
Benjamin Minixhofer Jonas Pfeiffer Ivan Vulić
Many NLP pipelines split text into sentences as one of the crucial
preprocessing steps. Prior sentence segmentation tools either rely on
punctuation or require a considerable amount of sentence-segmented training
data: both central assumptions might fail when porting sentence segmenters to
diverse languages on a massive scale. In this work, we thus introduce a
multilingual punctuation-agnostic sentence segmentation method, currently
covering 85 languages, trained in a self-supervised fashion on unsegmented
text, by making use of newline characters which implicitly perform segmentation
into paragraphs. We further propose an approach that adapts our method to the
segmentation in a given corpus by using only a small number (64-256) of
sentence-segmented examples. The main results indicate that our method
outperforms all the prior best sentence-segmentation tools by an average of
6.1% F1 points. Furthermore, we demonstrate that proper sentence segmentation
has a point: the use of a (powerful) sentence segmenter makes a considerable
difference for a downstream application such as machine translation (MT). By
using our method to match sentence segmentation to the segmentation used during
training of MT models, we achieve an average improvement of 2.3 BLEU points
over the best prior segmentation tool, as well as massive gains over a trivial
segmenter that splits text into equally sized blocks.