Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition

Abstract

To alleviate the shortage of labeled data, we propose to use bilingually-constrained synthetic implicit data for implicit discourse relation recognition. These data are extracted from a bilingual sentence-aligned corpus according to the implicit/explicit mismatch between different languages. Incorporating these data via a multi-task neural network model achieves significant improvements over baselines, on both the English PDTB and Chinese CDTB data sets.