  Pretrained language models are long known to be subpar in capturing sentence
and document-level semantics. Though heavily investigated, transferring
perturbation-based methods from unsupervised visual representation learning to
NLP remains an unsolved problem. This is largely due to the discreteness of
subword units brought by tokenization of language models, limiting small
perturbations of inputs to form semantics-preserved positive pairs. In this
work, we conceptualize the learning of sentence-level textual semantics as a
visual representation learning process. Drawing from cognitive and linguistic
sciences, we introduce an unsupervised visual sentence representation learning
framework, employing visually-grounded text perturbation methods like typos and
word order shuffling, resonating with human cognitive patterns, and enabling
perturbation to texts to be perceived as continuous. Our approach is further
bolstered by large-scale unsupervised topical alignment training and natural
language inference supervision, achieving comparable performance in semantic
textual similarity (STS) to existing state-of-the-art NLP methods.
Additionally, we unveil our method's inherent zero-shot cross-lingual
transferability and a unique leapfrogging pattern across languages during
iterative training. To our knowledge, this is the first representation learning
method devoid of traditional language models for understanding sentence and
document semantics, marking a stride closer to human-like textual
comprehension. Our code is available at
https://github.com/gowitheflow-1998/Pixel-Linguist
