Papers with discourse parser

4 papers
Evaluating Discourse in Structured Text Representations (P19-1)

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Challenge: Discourse structure is integral to understanding a text and is useful in many NLP tasks.
Approach: They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree.
Outcome: The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure.
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.
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)

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Challenge: a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time .
Approach: They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion .
Outcome: The proposed framework surpasses previous approaches on both tasks and human agreement on both.
Discourse Structure for the Minecraft Corpus (2024.lrec-main)

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Challenge: a discourse annotated version of the Minecraft Dialogue Corpus is a new linguistic resource for human-computer interaction . a recent study shows that inferring excecutable actions from language is difficult in the Minecraft setting .
Approach: They propose a discourse annotated version of the Minecraft Dialogue Corpus . they train a parser with a novel "2 pass architecture" that gives excellent results .
Outcome: The proposed model performs well on attachment prediction and relation labeling tasks especially long distance attachments.

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