Contrastive Language Adaptation for Cross-Lingual Stance Detection
Table 3: CLMN results on the target test dataset.
['[BOLD] Methods', '[BOLD] Weigh. Acc.', '[BOLD] Acc.', '[BOLD] Macro-F1']
, recall that α controls the balance between classification and language adaptation losses. Our further analysis shows that pretraining biases the model toward the source language. While the model without pretraining achieves its best performance with a large α (α=0.7), the model with pretraining performs well with a smaller α (α=0.3). This suggests that our model can capture the characteristics of the source dataset via pretraining when using small supervision from language adaptation (i.e., small α).