An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction

Fuente: arXiv
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Autori principali: Judge, Evi, Suchocki, Reece, Syed, Konner
Natura: Preprint
Pubblicazione: 2024
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author Judge, Evi
Suchocki, Reece
Syed, Konner
author_facet Judge, Evi
Suchocki, Reece
Syed, Konner
contents Discourse relation classification is an especially difficult task without explicit context markers (Prasad et al., 2008). Current approaches to implicit relation prediction solely rely on two neighboring sentences being targeted, ignoring the broader context of their surrounding environments (Atwell et al., 2021). In this research, we propose three new methods in which to incorporate context in the task of sentence relation prediction: (1) Direct Neighbors (DNs), (2) Expanded Window Neighbors (EWNs), and (3) Part-Smart Random Neighbors (PSRNs). Our findings indicate that the inclusion of context beyond one discourse unit is harmful in the task of discourse relation classification.
format Preprint
id arxiv_https___arxiv_org_abs_2405_09735
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction
Judge, Evi
Suchocki, Reece
Syed, Konner
Computation and Language
Discourse relation classification is an especially difficult task without explicit context markers (Prasad et al., 2008). Current approaches to implicit relation prediction solely rely on two neighboring sentences being targeted, ignoring the broader context of their surrounding environments (Atwell et al., 2021). In this research, we propose three new methods in which to incorporate context in the task of sentence relation prediction: (1) Direct Neighbors (DNs), (2) Expanded Window Neighbors (EWNs), and (3) Part-Smart Random Neighbors (PSRNs). Our findings indicate that the inclusion of context beyond one discourse unit is harmful in the task of discourse relation classification.
title An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction
topic Computation and Language
url https://arxiv.org/abs/2405.09735