Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

Text segmentation into sentences is essential for numerous NLP tasks, most notably because many models operate more effectively when input is separated into individual sentences. Existing methods, both rule-based and statistical, have often proven inadequate, especially in scenarios that involve missing punctuation, require adaptability across different domains, or necessitate high processing efficiency. This paper introduces "Segment Any Text" (SaT), a model purporting to offer robustness, adaptability, and efficiency in sentence segmentation, overcoming limitations in previous approaches.

Key Contributions

1. Robustness: SaT integrates a novel pretraining procedure that reduces reliance on punctuation. This is crucial for scenarios involving poorly punctuated texts or error-prone outputs from Automatic Speech Recognition (ASR) systems.
2. Adaptability: SaT introduces a fine-tuning stage allowing the model to adapt effectively to new domains with minimal data. This method, using parameter-efficient fine-tuning via LoRA, outperforms previous domain adaptation techniques.
3. Efficiency: Through architectural modifications, SaT achieves a threefold gain in processing speed over current state-of-the-art methods. This efficiency is evidenced in the reduced inference times measured across various corpora.

Methodology

SaT operates on subwords, avoiding the bottleneck associated with character-level models. The pretraining is conducted on multilingual data using a subword tokenizer and effectively predicts sentence boundaries without reliance on punctuation or language-specific rules. This stage involves a unique approach to data corruption: random removal of casing and punctuation, and the introduction of additional perturbations to simulate realistic input noise. The model further improves through a supervised fine-tuning stage using high-quality sentence-segmented corpora across multiple languages.

To address the variability in sentence boundaries across domains, SaT employs parameter-efficient fine-tuning via LoRA, significantly reducing the need for large, domain-specific datasets. For efficiency, SaT uses subword tokenization and processes tokens consisting of multiple characters, making it faster than character-level models.

Experimental Results

Evaluation across 85 languages using datasets from various domains reveals that:

• SaT outperforms rule-based, statistical, and existing machine learning-based methods including new LLM-based baselines such as Cohere's Command R and Meta's LLaMA.
• SaT achieves robustness against text corruption, outperforming previous methods in scenarios devoid of typical punctuation cues.
• In terms of adaptability, SaT's fine-tuning capabilities demonstrate superior performance in domain-specific tasks such as segmenting legal documents and lyrics, with performance surpassing domain-specific models using as few as 16 examples.
• Efficiency assessments reveal a threefold reduction in inference time while maintaining or improving segmentation accuracy, marking SaT as an efficient solution for real-world applications.

Implications and Future Directions

The research holds substantial implications for both theoretical and practical applications within the NLP community. The demonstrated robustness and efficiency make SaT an ideal candidate for deployment in real-time systems, notably in domains with irregular or noisy textual data. From a theoretical standpoint, SaT’s success underscores the importance of holistic training approaches that incorporate noise resilience and parameter-efficient adaptation.

Future developments may include:

• Extending the architecture to support more languages beyond the current 85, potentially using more diverse multilingual corpora such as MADLAD-400.
• Further optimization and benchmarking of the efficiency of SaT, specifically focusing on different hardware configurations to maximize real-world applicability.
• Exploring integration into larger NLP pipelines, assessing impact on downstream tasks like translation, summarization, and sentiment analysis.

In conclusion, SaT presents a significant advancement in the field of sentence segmentation, balancing robustness, efficiency, and adaptability. Its universal applicability across diverse languages and domains, combined with state-of-the-art performance, sets a new standard in text segmentation methodologies and paves the way for more resilient and efficient NLP systems.