Segmenting text into sentences plays an early and crucial role in many NLP systems.This is commonly achieved by using rule-based or statistical methods relying on lexical features such as punctuation.Although some recent works no longer exclusively rely on punctuation, we find that no prior method achieves all of (i) robustness to missing punctuation, (ii) effective adaptability to new domains, and (iii) high efficiency.We introduce a new model -Segment any Text (SAT) -to solve this problem.To enhance robustness, we propose a new pretraining scheme that ensures less reliance on punctuation.To address adaptability, we introduce an extra stage of parameter-efficient fine-tuning, establishing state-of-the-art performance in distinct domains such as verses from lyrics and legal documents.Along the way, we introduce architectural modifications that result in a threefold gain in speed over the previous state of the art and solve spurious reliance on context far in the future.Finally, we introduce a variant of our model with fine-tuning on a diverse, multilingual mixture of sentence-segmented data, acting as a drop-in replacement and enhancement for existing segmentation tools.Overall, our contributions provide a universal approach for segmenting any text.Our method outperforms all baselines -including strong LLMs -across 8 corpora spanning diverse domains and languages, especially in practically relevant situations where text is poorly formatted.1

Language models frequently inherit societal biases from their training data.Numerous techniques have been proposed to mitigate these biases during both the pre-training and finetuning stages.However, fine-tuning a pretrained debiased language model on a downstream task can reintroduce biases into the model.Additionally, existing debiasing methods for downstream tasks either (i) require labels of protected attributes (e.g., age, race, or political views) that are often not available or (ii) rely on indicators of bias, which restricts their applicability to gender debiasing since they rely on gender-specific words.To address this, we introduce a novel debiasing regularization technique based on the class-wise variance of embeddings.Crucially, our method does not require attribute labels and targets any attribute, thus addressing the shortcomings of existing debiasing methods.Our experiments on encoder language models and three datasets demonstrate that our method outperforms existing strong debiasing baselines that rely on target attribute labels while maintaining performance on the target task.1