Impact of Model Size on Zero-Shot Cross-Lingual Transfer Performance in Text Classification

Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zero-shot cross-lingual transfer capabilities and performs surprisingly well on zero-shot POS tagging and Named Entity Recognition (NER), as well as on cross-lingual model transfer. At present, the mainstream methods to solve the cross-lingual downstream tasks are always using the last transformer layer's output of mBERT as the representation of linguistic information. In this work, we explore the complementary property of lower layers to the last transformer layer of mBERT. A feature aggregat

Research goal: What is the impact of model size on zero-shot cross-lingual transfer performance in tasks like text classification, comparing F1 scores of small (e.g., 100M parameters) vs. large (e.g., 1B+ parameters) multilingual models on the PAWS-X dataset?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.