On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing

Word order is a significant distinctive feature to differentiate languages. In this paper, we investigate cross-lingual transfer and posit that an order-agnostic model will perform better when transferring to distant foreign languages. To test our hypothesis, we train dependency parsers on an English corpus and evaluate their transfer performance on 30 other languages. Specifically, we compare encoders and decoders based on Recurrent Neural Networks (RNNs) and modified self-attentive architectures. The former rely on sequential information while the latter are more flexible at modeling token order. Detailed analysis shows that RNN-based architectures transfer well to languages that are close to English, while self-attentive models have better overall cross-lingual transferability and perform especially well on distant languages.