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natural-language-processing

Implicit Discourse Relation Classification

3260 papers • 126 benchmarks • 313 datasets

Parsing a text into a set of discourse relations between two adjacent or non-adjacent discourse units in the absence of explicit connectives, such as 'but' or 'however', and classifying those relations. (Source: Adapted from https://www.cs.brandeis.edu/~clp/conll15st/intro.html)

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Benchmarks

These leaderboards are used to track progress in implicit-discourse-relation-classification

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Dataset

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Libraries

Use these libraries to find implicit-discourse-relation-classification models and implementations

Datasets

DISRPT2021

Dissonance Twitter Dataset

Subtasks

No subtasks available.

Most implemented papers

Implicit Discourse Relation Identification for Open-domain Dialogues

JiaQi Wu, M. Walker, Mingyu Derek Ma, Kevin K. Bowden, Wen Cui•Mon Jul 08 2019

A method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns is proposed, resulting in a novel corpus of discourse relation pairs, the first of its kind to attempt to identify the discourse relations connecting the dialogicturns in open-domain discourse.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

Paper Graph

A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models

Jacob Eisenstein, Gholamreza Haffari, Yangfeng Ji•Mon Feb 29 2016

A novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences that outperforms state-of-the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act Classification in the Switchboard corpus.

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Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish, Xiaoran Liu, Jonah Luby, C. Luhmann, H. A. Schwartz•Tue May 02 2023

This work proposes and investigates transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach.

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Zero-shot transfer for implicit discourse relation classification

Murathan Kurfali, Robert Östling•Mon Jul 29 2019

This work presents a new system using zero-shot transfer learning for implicit discourse relation classification, where the only resource used for the target language is unannotated parallel text.

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Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation

M. Strube, Wei Liu•Fri Jun 09 2023

An end-to-end neural model is designed to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB, which significantly outperforms various baselines on three datasets, demonstrating its superiority for thetask.

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On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

Yangqiu Song, Xin Liu, Jiefu Ou, Xin Jiang•Sun Apr 26 2020

This work argues that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis and proposes a novel model to combine these modules together.

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Implicit Discourse Relation Classification | State-of-the-Art