Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles
This addresses the problem of limited linguistic resources for Hebrew NLP, though it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of a semantic role labeling (SRL) resource for Hebrew by building one semi-automatically from parallel movie subtitles, and they trained a neural model that provides the first baseline for Hebrew SRL.
We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English. This corpus is derived from the multilingual OpenSubtitles dataset and includes short informal sentences, for which reliable linguistic annotations have been computed. We provide a fully annotated version of the data including morphological analysis, dependency syntax and semantic role labeling in both FrameNet and PropBank styles. Sentences are aligned between English and Hebrew, both sides include full annotations and the explicit mapping from the English arguments to the Hebrew ones. We train a neural SRL model on this Hebrew resource exploiting the pre-trained multilingual BERT transformer model, and provide the first available baseline model for Hebrew SRL as a reference point. The code we provide is generic and can be adapted to other languages to bootstrap SRL resources.