Comparative Performance of mBERT and XLM-R on Zero-Shot Cross-Lingual Semantic Parsing via Partial Match Scores on MLQA

The availability of corpora to train semantic parsers in English has lead to significant advances in the field. Unfortunately, for languages other than English, annotation is scarce and so are developed parsers. We then ask: could a parser trained in English be applied to language that it hasn't been trained on? To answer this question we explore zero-shot cross-lingual semantic parsing where we train an available coarse-to-fine semantic parser (Liu et al., 2018) using cross-lingual word embeddings and universal dependencies in English and test it on Italian, German and Dutch. Results on the P

Research goal: How does the performance of mBERT and XLM-R compare on zero-shot cross-lingual semantic parsing when evaluated using partial match scores instead of exact match scores on the MLQA benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.