ELRA and the Association for Computational Linguistics
Abstract
In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certainsyntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifyingand resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavilyonsemanticcoherenceandlexicalrelationships. WeproposeaBERT-basedcross-lingualmodelforzeropronounresolution,andevaluateit on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolutionfor Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction andfine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating whichlayer encodes the most suitable representation for the task. Our code is available at https://github.com/amaloraini/cross-lingual-Z
