Wals Roberta Sets -

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Higher ( \lambda ) (e.g., 0.1–1.0) forces the factorization to rely more on the RoBERTa prior. Lower ( \lambda ) (e.g., 0.001) allows more deviation based on observed interactions. wals roberta sets

[Input Text Tokens] │ [RoBERTa Base/Large] ├── Layer 1 ──> (Weight_1) ──┐ ├── Layer 2 ──> (Weight_2) ──┼──> [Weighted Sum Aggregation] ──> [Classification Head] ├── Layer ...──> (Weight_...) ─┤ └── Layer N ──> (Weight_N) ──┘

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Probing tasks reveal that RoBERTa is significantly better at predicting syntactic WALS sets (like word order) than phonological sets. This is expected, as the input to RoBERTa is text (tokens/subwords), lacking direct acoustic signal. The model infers syntax through the sequential ordering of tokens, making syntactic WALS features recoverable. [Input Text Tokens] │ [RoBERTa Base/Large] ├── Layer

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