On the Relation between Syntactic Divergence and Zero-Shot Performance
This work addresses a gap in understanding syntactic transfer for zero-shot parsing, which is incremental but provides insights for multilingual NLP applications.
The study investigated the relationship between syntactic divergence in translation and zero-shot parsing performance, finding a strong link between cross-lingual stability and improved parsing accuracy, with extrinsic evaluation on relation extraction tasks in Russian and Korean.
We explore the link between the extent to which syntactic relations are preserved in translation and the ease of correctly constructing a parse tree in a zero-shot setting. While previous work suggests such a relation, it tends to focus on the macro level and not on the level of individual edges-a gap we aim to address. As a test case, we take the transfer of Universal Dependencies (UD) parsing from English to a diverse set of languages and conduct two sets of experiments. In one, we analyze zero-shot performance based on the extent to which English source edges are preserved in translation. In another, we apply three linguistically motivated transformations to UD, creating more cross-lingually stable versions of it, and assess their zero-shot parsability. In order to compare parsing performance across different schemes, we perform extrinsic evaluation on the downstream task of cross-lingual relation extraction (RE) using a subset of a popular English RE benchmark translated to Russian and Korean. In both sets of experiments, our results suggest a strong relation between cross-lingual stability and zero-shot parsing performance.