首页|Chinese Macro Discourse Parsing on Generative Fusion and Distant Supervision
Chinese Macro Discourse Parsing on Generative Fusion and Distant Supervision
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NETL
Most previous studies on discourse parsing have utilized discriminative models to construct tree structures。 However， these models tend to overlook the global perspective of the tree structure as a whole during the step-by-step top-down or bottom-up parsing process。 To address this issue， we propose DP-GF， a macro Discourse Parser based on Generative Fusion， which considers discourse parsing from both process-oriented and result-oriented perspectives。 Additionally， due to the small size of existing corpora and the difficulty in annotating macro discourse structures， DP-GF addresses the small-sample problems by proposing a distant supervision training method that transforms a relatively large-scale topic structure corpus into a high-quality silver-standard discourse structure corpus。 Our experimental results on MCDTB 2。0 demonstrate that our proposed model outperforms the state-of-the-art baselines on discourse tree construction。
School of Computer Science and Technology, Soochow University, Suzhou, China
School of Data Science, The Chinese University of Hong Kong, Shenzhen, China##School of Information Science and Technology, University of Science and Technology of China, Hefei, China
Pacific rim international conference on artificial intelligence
NETL
