arXiv AI By Ahmad Pouramini, Mahsa Afsharizadeh

Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning

Read the original on arXiv AI →

arXiv:2607. 19845v1 Announce Type: cross Abstract: This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

Hugging Face Trending Papers
Jul 22

Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning

This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the latent factual structure of natural language sentences. The proposed method identifies the semantic boundary between a descriptive prefix (head) and its factual completion (tail) by formulating sentence splitting as a discrete segmentation problem, where a sentence of length $N$ admits $N$ possible split points but only one recovers the intended head--tail structure.