Wals Roberta Sets Upd !!exclusive!! | Windows |

WALS RoBERTa Sets (commonly found as WALS-RoBERTa-Sets-1-36.zip

Key Dependencies for WALS:

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• Learn scaling factors ( w_i ) using labeled STS data (contrastive loss).
• Keeps the interpretability of dimension weighting while adapting to task.

• Early fusion: concatenate projected RoBERTa embedding with projected WALS vector, followed by MLP.
• Late fusion: run separate MLPs on RoBERTa and WALS, then combine with gated-sum or attention.
• Conditional adapter: condition lightweight adapter layers inside RoBERTa on WALS embeddings (FiLM/feature-wise linear modulation).
• Multitask/auxiliary loss: predict selected WALS features from RoBERTa representation to encourage typological information encoding.