Papers with Discourse parsing

3 papers
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)

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Challenge: Discourse parsing accuracy degrades significantly on out-of-domain text.
Approach: They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision.
Outcome: The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement.
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank (C18-1)

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Challenge: Discourse parsing is a challenging task and plays a critical role in discourse analysis.
Approach: They propose a macro discourse structure presentation schema to present the macro level discourse structure analysis.
Outcome: The proposed corpus is based on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition.
Predicting Discourse Structure using Distant Supervision from Sentiment (D19-1)

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Challenge: Discourse parsing is a fundamental NLP task known to enhance key downstream tasks, such as sentiment analysis, text classification and summarization.
Approach: They propose a method that uses document supervision to generate abundant data for RST-style discourse structure prediction by using an optimal CKY-style tree generation algorithm.
Outcome: The proposed approach performs well on the more difficult task of inter-domain discourse structure prediction, but it does not match the performance of a parser trained and tested on the same dataset.

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