Year: 2016
Type: conference-paper
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.
DOI: 10.18653/v1/d16-1253
Institution: Xiamen University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2561937255