Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (2305.18893v1)

Abstract: 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.

• The paper introduces a novel self-supervised method that uses newline cues to segment sentences without relying on punctuation.
• The bidirectional character-level model adapts with minimal labeled data (64-256 examples), achieving an average 6.1% F1 score improvement.
• The approach improves downstream machine translation by 2.3 BLEU points and supports segmentation across 85 diverse languages.

Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation

The paper introduces a novel method for sentence segmentation in NLP that transcends traditional reliance on punctuation and extensive labeled data, offering a solution applicable to a broad range of languages. The proposed method, termed "Where's the Point" (WtP), is adaptable to 85 languages and is trained using a self-supervised approach by leveraging newline characters as implicit paragraph delimiters.

Methodology

The core innovation of WtP lies in its self-supervised training paradigm. The authors harnessed newline characters to signal paragraph endings, thus sidestepping reliance on traditional punctuation marks. The model, a bidirectional character-level LLM (ChLM), was trained to predict the likelihood of a newline following a character. This approach transforms sentence segmentation into a character-level prediction task.

In addition, WtP introduces a data-efficient adaptation procedure that fine-tunes the model on a target corpus with the aid of minimal labeled data (64-256 sentence-segmented examples). By incorporating an auxiliary objective that predicts punctuation within text, the model can better adapt to diverse corpus-specific definitions of sentence boundaries.

Experimental Results

Empirical results indicate a notable improvement over existing sentence segmentation tools, with WtP achieving an average of 6.1% F1 score increase across several benchmarks. Additionally, it facilitates a 2.3 BLEU point improvement in downstream machine translation tasks when matched to the segmentation used in model training, highlighting its practical efficacy.

Implications and Future Work

Practically, WtP's ability to operate without extensive language-specific assumptions or large labeled datasets means that NLP systems can be more easily and broadly deployed across languages and domains. Theoretically, it underscores the viability of character-level models in handling tasks traditionally tied to token-level approaches, highlighting a potential shift toward more robust, multilingual language processing frameworks.

The paper suggests future exploration into further improving low-resource language performance and possible enhancements via cross-lingual transfer and domain adaptation. Potential developments could involve expanding character-level modeling applications or refining auxiliary tasks to bolster segmentation accuracy in challenging contexts.

In conclusion, WtP offers a significant advancement in sentence segmentation capabilities across languages, bridging gaps in existing methodologies and setting the stage for refined multilingual NLP applications.