Leveraging bilingually-constrained synthetic data via multi-task neural networks for implicit discourse relation recognition

Abstract

Recognizing implicit discourse relations is an important but challenging task in discourse understanding. To alleviate the shortage of labeled data, previous work automatically generates synthetic implicit data (SynData) as additional training data, by removing connectives from explicit discourse instances. Although SynData has been proven useful for implicit discourse relation recognition, it also has the meaning shift problem and the domain problem. In this paper, we first propose to use bilingually-constrained synthetic implicit data (BiSynData) to enrich the training data, which can alleviate the drawbacks of SynData. Our BiSynData is constructed from a bilingual sentence-aligned corpus according to the implicit/explicit mismatch between different languages. Then we design a multi-task neural network model to incorporate our BiSynData to benefit implicit discourse relation recognition. Experimental results on both the English PDTB and Chinese CDTB data sets show that our proposed method achieves significant improvements over baselines using SynData.