Universal dependency relation features, but not explicit dependency tree structure, improve zero-shot cross-lingual semantic parsing on the Parallel Meaning Bank.
Neural Networks for Cross-lingual Negation Scope Detection
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abstract
Negation scope has been annotated in several English and Chinese corpora, and highly accurate models for this task in these languages have been learned from these annotations. Unfortunately, annotations are not available in other languages. Could a model that detects negation scope be applied to a language that it hasn't been trained on? We develop neural models that learn from cross-lingual word embeddings or universal dependencies in English, and test them on Chinese, showing that they work surprisingly well. We find that modelling syntax is helpful even in monolingual settings and that cross-lingual word embeddings help relatively little, and we analyse cases that are still difficult for this task.
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A survey of cross-lingual features for zero-shot cross-lingual semantic parsing
Universal dependency relation features, but not explicit dependency tree structure, improve zero-shot cross-lingual semantic parsing on the Parallel Meaning Bank.