首页|Bidirectional Macro-level Discourse Parser Based on Oracle Selection
Bidirectional Macro-level Discourse Parser Based on Oracle Selection
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NETL
Springer Nature
Most existing studies construct a discourse structure tree following two popular methods: top-down or bottom-up strategy。 However， they often suffered from cascading errors because they can not switch the strategy of building a structure tree to avoid mistakes caused by uncertain decision-making。 Moreover， due to the different basis of top-down and bottom-up methods in building discourse trees， thoroughly combining the advantages of the two methods is challenging。 To alleviate these issues， we propose a Bidirectional macro-level discourse Parser based on OracLe selEction (BIPOLE)， which combines the top-down and bottom-up strategies by selecting the suitable decision-making strategy。 BIPOLE consists of a basic parsing module composed of top-down and bottom-up sub-parsers and a decision-maker for selecting a prediction strategy by considering each sub-parser state。 Moreover， we propose a label-based data-enhanced oracle training strategy to generate the training data of the decision-maker。 Experimental results on MCDTB and RST-DT show that our model can effectively alleviate cascading errors and outperforms the SOTA baselines significantly。
NETL
Springer Nature
