Banyan: Improved Representation Learning with Explicit Structure

We present Banyan, an improved model to learn semantic representations by inducing explicit structure over data. In contrast to prior approaches using structure spanning single sentences, Banyan learns by resolving multiple constituent structures into a shared one explicitly incorporating global context. Combined with an improved message-passing scheme inspired by Griffin, Banyan learns significantly better representations, avoids spurious false negatives with contrastive learning, and drastically improves memory efficiency in such explicit-structured models. Using the Self-StrAE framework, we show that Banyan (a) outperforms baselines using sentential structure across various settings (b) matches or outperforms unstructured baselines like GloVe (+augmentations) and a RoBERTa medium (+simcse) pre-trained on 100M tokens, despite having just a handful of (non-embedding) parameters, and (c) also learns effective representations across several low resource (Asian and African) languages as measured on SemRel tasks.