A survey of cross-lingual features for zero-shot cross-lingual semantic parsing
This addresses the challenge of semantic parsing for non-English languages with limited data, but it is incremental as it builds on existing methods and datasets.
The paper tackled the problem of training semantic parsers for languages with scarce annotation by exploring zero-shot cross-lingual semantic parsing, using English-trained parsers on Italian, German, and Dutch, and found that Universal Dependency features significantly boost performance when combined with lexical features.
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 Parallel Meaning Bank - a multilingual semantic graphbank, show that Universal Dependency features significantly boost performance when used in conjunction with other lexical features but modelling the UD structure directly when encoding the input does not.