An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models

The capacity and effectiveness of pre-trained multilingual models (MLMs) for zero-shot cross-lingual transfer is well established. However, phenomena of positive or negative transfer, and the effect of language choice still need to be fully understood, especially in the complex setting of massively multilingual LMs. We propose an efficient method to study transfer language influence in zero-shot performance on another target language. Unlike previous work, our approach disentangles downstream tasks from language, using dedicated adapter units. Our findings suggest that some languages do not largely affect others, while some languages, especially ones unseen during pre-training, can be extremely beneficial or detrimental for different target languages. We find that no transfer language is beneficial for all target languages. We do, curiously, observe languages previously unseen by MLMs consistently benefit from Figure 1: Our approach uses efficient few-step continued transfer from almost any language. We additionally tuning (left) and adapter modules (right) to disentangle use our modular approach to quantify the effect of task and language to quantify the effect negative interference efficiently and catagorize of a transfer language for a given task and model.