Multilingual Semantic Parsing with a Pipeline of Linear Classifiers

I describe a fast multilingual parser for semantic dependencies. The parser is implemented as a pipeline of linear classifiers trained with support vector machines. I use only first order features, and no pair-wise feature combinations in order to reduce training and prediction times. Hyper-parameters are carefully tuned for each language and sub-problem. The system is evaluated on seven different languages: Catalan, Chinese, Czech, English, German, Japanese and Spanish. An analysis of learning rates and of the reliance on syntactic parsing quality shows that only modest improvements could be expected for most languages given more training data; Better syntactic parsing quality, on the other hand, could greatly improve the results. Individual tuning of hyper-parameters is crucial for obtaining good semantic parsing quality