mlsanityData to 14 Oct 2026

An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction

arXiv 2405.09735 ↗First posted 16 May 2024cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 16 May 2024.

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8 of 18 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  4. 2018Deep Enhanced Representation for Implicit Discourse Relation Recognitioncited by 21
  5. 2021A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognitioncited by 18
  6. 2018Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraphcited by 17
  7. 2020Extending Implicit Discourse Relation Recognition to the PDTB-3cited by 10
  8. 2022Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicitscited by 4

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