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.
|Original language||American English|
|Title of host publication||EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||15|
|State||Published - 2021|
|Event||2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021 - Virtual, Punta Cana, Dominican Republic|
Duration: 7 Nov 2021 → 11 Nov 2021
|Name||EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings|
|Conference||2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021|
|City||Virtual, Punta Cana|
|Period||7/11/21 → 11/11/21|
Bibliographical noteFunding Information:
This work was supported by the Israel Science Foundation (grant no. 929/17). Taelin Karidi was partially supported by a fellowship from the Hebew University Center for Interdisciplinary Data Science Research.
© 2021 Association for Computational Linguistics