A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extraction

• URL: http://arxiv.org/abs/2608.23235v1
• Date: Mon, 24 Aug 2026 13:24:24 GMT
• Title: A Multi-Domain and Multi-Task Generative Framework with Explicit Task and Domain Conditioning for Cross-Domain Event Extraction
• Abstract summary: Event extraction aims to identify event triggers, classify event types, and extract arguments to construct structured event representations.<n>We propose a unified multi-domain and multi-task training framework that models heterogeneous event schemas within a single model.
• Score: 4.169293754839175
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Event extraction aims to identify event triggers, classify event types, and extract arguments to construct structured event representations. Despite strong in-domain performance, developing models that generalize robustly across domains remains challenging due to variations in contextual expressions and event schemas. Prior unified and multi-task approaches improve in-domain accuracy but exhibit limited flexibility when applied to unseen domains. Even large language model-based methods that provide full event ontologies at inference time often underperform compared to smaller, task-specific fine-tuned models. We propose a unified multi-domain and multi-task training framework that models heterogeneous event schemas within a single model. Our approach introduces domain conditioning signals, jointly with task-specific prompts, enabling dynamic adaptation to dataset-specific schemas without requiring complete event label sets at inference time. The framework supports both pipeline and end-to-end extraction settings, facilitating efficient task- and domain-level transfer. Experiments on diverse event extraction benchmarks demonstrate that our method achieves competitive performance, strong cross-domain generalization, and practical scalability, while preserving domain-specific precision.

Related papers

• A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework [3.816538153840492]
  We propose a unified generative sequence-to-sequence framework that performs event extraction subtasks jointly.<n>We fine-tune pretrained language models on multiple event datasets across diverse domains.<n>We demonstrate these capabilities through a web-based application tailored for researchers and practitioners.
  arXiv  Detail & Related papers  (2026-08-24T13:51:03Z)
• A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents [0.0]
  We present MODEE, a novel approach for open-domain event extraction.<n>It combines graph-based learning with text-based representation from LLMs to model document-level reasoning.<n> Empirical evaluations on large datasets demonstrate that MODEE outperforms state-of-the-art open-domain event extraction approaches.
  arXiv  Detail & Related papers  (2026-04-23T17:33:44Z)
• Human-in-Context: Unified Cross-Domain 3D Human Motion Modeling via In-Context Learning [64.30639042094548]
  We propose a new setting to train a unified cross-domain model through a single process.<n>We first introduce Pose-in-Context (PiC), which leverages in-context learning to create a pose-centric cross-domain model.<n>We thus propose Human-in-Context (HiC), an extension of PiC that broadens generalization across modalities, tasks, and datasets.
  arXiv  Detail & Related papers  (2025-08-14T17:59:23Z)
• Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models [5.466962214217334]
  Supervised fine-tuning (SFT) is widely used to align large language models (LLMs) with information extraction (IE) tasks, such as named entity recognition (NER)<n>We propose the SaM framework, which dynamically Selects and Merges expert models at inference time.
  arXiv  Detail & Related papers  (2025-06-28T08:28:52Z)
• Exploiting Aggregation and Segregation of Representations for Domain Adaptive Human Pose Estimation [50.31351006532924]
  Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc.<n>It suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation.<n>We introduce a novel framework that capitalizes on both representation aggregation and segregation for domain adaptive human pose estimation.
  arXiv  Detail & Related papers  (2024-12-29T17:59:45Z)
• Unified Language-driven Zero-shot Domain Adaptation [55.64088594551629]
  Unified Language-driven Zero-shot Domain Adaptation (ULDA) is a novel task setting. It enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
  arXiv  Detail & Related papers  (2024-04-10T16:44:11Z)
• Prompting Diffusion Representations for Cross-Domain Semantic Segmentation [101.04326113360342]
  diffusion-pretraining achieves extraordinary domain generalization results for semantic segmentation. We introduce a scene prompt and a prompt randomization strategy to help further disentangle the domain-invariant information when training the segmentation head.
  arXiv  Detail & Related papers  (2023-07-05T09:28:25Z)
• Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction [15.02043375212839]
  This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score.
  arXiv  Detail & Related papers  (2023-06-30T15:22:57Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing [51.81533991497547]
  Task-oriented compositional semantic parsing (TCSP) handles complex nested user queries. We present X2 compared a transferable Cross-lingual and Cross-domain for TCSP. We propose to predict flattened intents and slots representations separately and cast both prediction tasks into sequence labeling problems.
  arXiv  Detail & Related papers  (2021-06-07T16:40:05Z)

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.