Relational Prompt-based Pre-trained Language Models for Social Event Detection

• URL: http://arxiv.org/abs/2404.08263v2
• Date: Tue, 10 Sep 2024 09:14:49 GMT
• Title: Relational Prompt-based Pre-trained Language Models for Social Event Detection
• Authors: Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu,
• Abstract summary: Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. We propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences, and a new multi-relational prompt-based message learning mechanism. We show that the RPLM_SED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail
• Score: 45.574891451459955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM_SED on three real-world datasets, demonstrating that the RPLM_SED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.

Related papers

• Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models [28.416254061159176]
  Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods.<n>Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios.<n>We propose leveraging vision-language models (VLMs) for the MMKP task.
  arXiv  Detail & Related papers  (2025-10-10T13:13:07Z)
• Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model [20.002086667357595]
  We propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into language models.<n> PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection.<n>We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo)
  arXiv  Detail & Related papers  (2025-08-10T07:04:50Z)
• CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass [3.0566617373924325]
  Recent advances in pre-trained language models (PLMs) have driven remarkable progress in this field. We propose CSE-SFP, an innovative method that exploits the structural characteristics of generative models. We show that CSE-SFP not only produces higher-quality embeddings but also significantly reduces both training time and memory consumption.
  arXiv  Detail & Related papers  (2025-05-01T08:27:14Z)
• Graphically Speaking: Unmasking Abuse in Social Media with Conversation Insights [10.188075925271471]
  Abusive language in social media conversations depends on the conversational context, characterized by the content and topology of preceding comments. Traditional Abusive Language Detection models often overlook this context, which can lead to unreliable performance metrics. Recent Natural Language Processing (NLP) methods that integrate conversational context often depend on limited and simplified representations, and report inconsistent results. We propose a novel approach that utilize graph neural networks (GNNs) to model social media conversations as graphs, where nodes represent comments, and edges capture reply structures.
  arXiv  Detail & Related papers  (2025-04-02T17:03:37Z)
• Towards Online Multi-Modal Social Interaction Understanding [36.37278022436327]
  We propose an online MMSI setting, where the model must resolve MMSI tasks using only historical information, such as recorded dialogues and video streams. We develop a novel framework, named Online-MMSI-VLM, that leverages two complementary strategies: multi-party conversation forecasting and social-aware visual prompting. Our method achieves state-of-the-art performance and significantly outperforms baseline models, indicating its effectiveness on Online-MMSI.
  arXiv  Detail & Related papers  (2025-03-25T17:17:19Z)
• Multi-Modal Self-Supervised Semantic Communication [52.76990720898666]
  We propose a multi-modal semantic communication system that leverages multi-modal self-supervised learning to enhance task-agnostic feature extraction. The proposed approach effectively captures both modality-invariant and modality-specific features while minimizing training-related communication overhead. The findings underscore the advantages of multi-modal self-supervised learning in semantic communication, paving the way for more efficient and scalable edge inference systems.
  arXiv  Detail & Related papers  (2025-03-18T06:13:02Z)
• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Unsupervised Pre-training with Language-Vision Prompts for Low-Data Instance Segmentation [105.23631749213729]
  We propose a novel method for unsupervised pre-training in low-data regimes. Inspired by the recently successful prompting technique, we introduce a new method, Unsupervised Pre-training with Language-Vision Prompts. We show that our method can converge faster and perform better than CNN-based models in low-data regimes.
  arXiv  Detail & Related papers  (2024-05-22T06:48:43Z)
• Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection [61.87480191351659]
  Graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs.
  arXiv  Detail & Related papers  (2023-12-19T06:28:32Z)
• FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction [49.510163437116645]
  Click-through rate (CTR) prediction plays as a core function module in personalized online services. Traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality. Pretrained Language Models(PLMs) has given rise to another paradigm, which takes as inputs the sentences of textual modality. We propose to conduct Fine-grained feature-level ALignment between ID-based Models and Pretrained Language Models(FLIP) for CTR prediction.
  arXiv  Detail & Related papers  (2023-10-30T11:25:03Z)
• RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning [33.86277578441437]
  Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in partially observable Markov Decision Processes. We propose the Return-Gap-Minimization Communication (RGMComm) algorithm, which is a surprisingly simple design of discrete message generation functions. Evaluations show that RGMComm significantly outperforms state-of-the-art multi-agent communication baselines.
  arXiv  Detail & Related papers  (2023-08-07T07:26:55Z)
• SDCUP: Schema Dependency-Enhanced Curriculum Pre-Training for Table Semantic Parsing [19.779493883522072]
  This paper designs two novel pre-training objectives to impose the desired inductive bias into the learned representations for table pre-training. We propose a schema-aware curriculum learning approach to mitigate the impact of noise and learn effectively from the pre-training data in an easy-to-hard manner.
  arXiv  Detail & Related papers  (2021-11-18T02:51:04Z)
• ST-BERT: Cross-modal Language Model Pre-training For End-to-end Spoken Language Understanding [23.367329217151084]
  We introduce a cross-modal pre-trained language model, called Speech-Text BERT (ST-BERT), to tackle end-to-end spoken language understanding tasks. Taking phoneme posterior and subword-level text as an input, ST-BERT learns a contextualized cross-modal alignment. Our method shows further SLU performance gain via domain-adaptive pre-training with domain-specific speech-text pair data.
  arXiv  Detail & Related papers  (2020-10-23T10:28:20Z)
• Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection [43.662221486962274]
  We propose a novel joint learning framework for Speech Activity Detection (SAD) We utilise generative adversarial networks to automatically learn a loss function for joint prediction of the frame-wise speech/ non-speech classifications together with the next audio segment. We evaluate the proposed framework on multiple public benchmarks, including NIST OpenSAT' 17, AMI Meeting and HAVIC.
  arXiv  Detail & Related papers  (2020-04-02T02:33:13Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.