Impact of Task Similarity on Zero-Shot Cross-Lingual Transfer Performance

In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source of cross-lingual transfer can be as straightforward as lexical overlap between languages (e.g., use of the same scripts, shared subwords) that naturally forces text embeddings to occupy a similar representation space. Recently introduced cross-lingual language model (XLM) pretraining brings out neural parameter sharing in Transformer-style networks as the most important factor for the transfer. In this paper, we aim

Research goal: What is the impact of task similarity between intermediate English tasks and target non-English tasks on zero-shot cross-lingual transfer performance, as measured by accuracy on XQuAD and other language understanding benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.