  Semantic role labeling (SRL) identifies predicate-argument structure(s) in a
given sentence. Although different languages have different argument
annotations, polyglot training, the idea of training one model on multiple
languages, has previously been shown to outperform monolingual baselines,
especially for low resource languages. In fact, even a simple combination of
data has been shown to be effective with polyglot training by representing the
distant vocabularies in a shared representation space. Meanwhile, despite the
dissimilarity in argument annotations between languages, certain argument
labels do share common semantic meaning across languages (e.g. adjuncts have
more or less similar semantic meaning across languages). To leverage such
similarity in annotation space across languages, we propose a method called
Cross-Lingual Argument Regularizer (CLAR). CLAR identifies such linguistic
annotation similarity across languages and exploits this information to map the
target language arguments using a transformation of the space on which source
language arguments lie. By doing so, our experimental results show that CLAR
consistently improves SRL performance on multiple languages over monolingual
and polyglot baselines for low resource languages.
