Wals Roberta Sets [hot] -

The intersection of linguistic typology and Natural Language Processing (NLP) has given rise to a critical question: Do deep learning models, specifically transformer-based architectures like RoBERTa, learn to represent the structural diversity of human language in a way that mirrors linguistic theory? This paper explores the relationship between the World Atlas of Language Structures (WALS) and the internal representations of RoBERTa . We analyze how models organize languages into "sets" based on structural features, the methodology for probing these representations, and the implications for multilingual NLP.

def get_roberta_set(texts, pool_strategy="mean"): inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True) with torch.no_grad(): outputs = model(**inputs) if pool_strategy == "cls": return outputs.last_hidden_state[:, 0, :].numpy() elif pool_strategy == "mean": return outputs.last_hidden_state.mean(dim=1).numpy() wals roberta sets

In code, this means:

: Designed for natural language understanding (NLU) tasks like sentiment analysis, question answering, and text classification. Intersection: Probing Models for Typological Features The intersection of linguistic typology and Natural Language