mlsanityData to 14 Oct 2026

Semi-supervised News Discourse Profiling with Contrastive Learning

arXiv 2309.11692 ↗First posted 20 Sep 2023cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

News Discourse Profiling seeks to scrutinize the event-related role of each sentence in a news article and has been proven useful across various downstream applications. Specifically, within the context of a given news discourse, each sentence is assigned to a pre-defined category contingent upon its depiction of the news event structure. However, existing approaches suffer from an inadequacy of available human-annotated data, due to the laborious and time-intensive nature of generating discourse-level annotations. In this paper, we present a novel approach, denoted as Intra-document Contrastive Learning with Distillation (ICLD), for addressing the news discourse profiling task, capitalizing on its unique structural characteristics. Notably, we are the first to apply a semi-supervised methodology within this task paradigm, and evaluation demonstrates the effectiveness of the presented approach.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 49 references that are in the map. First posted on arXiv 20 Sep 2023.

Builds on

28 of 49 references.

References · 28

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  7. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  8. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  9. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  10. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  11. 2020Longformer: The Long-Document Transformercited by 3,413
  12. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
16 more
  1. 2020Pre-trained Models for Natural Language Processing: A Surveycited by 505
  2. 2015Attention-Based Models for Speech Recognitioncited by 401
  3. 2020Keystroke Dynamics as Part of Lifeloggingcited by 334
  4. 2020DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representationscited by 251
  5. 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 193
  6. 2020CLEAR: Contrastive Learning for Sentence Representationcited by 186
  7. 2022A Review on Language Models as Knowledge Basescited by 157
  8. 2022DiffCSE: Difference-based Contrastive Learning for Sentence Embeddingscited by 132
  9. 2021Supporting Clustering with Contrastive Learningcited by 90
  10. 2022InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddingscited by 26
  11. 2021Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigmcited by 21
  12. 2022EASE: Entity-Aware Contrastive Learning of Sentence Embeddingcited by 21
  13. 2023Composition-contrastive Learning for Sentence Embeddingscited by 14
  14. 2022Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsingcited by 2
  15. 2022Modeling Document-level Temporal Structures for Building Temporal Dependency Graphscited by 2
  16. 2023RST-style Discourse Parsing Guided by Document-level Content Structurescited by 1

Built on by

Not cited yet.