The shared model allows utilizing sense correlations across words and therefore allows to transfer common disambiguation rules learned from disambiguating annotation-rich words and applies the rules for improving the disambiguation of annotation-lean words that share a sense alternation pattern.
we build a single neural network model for WSD that derives sense representations and meanwhile enforces congruence between a word instance and its right sense, by using both lexical resources and sense-annotated data.