This work proposes a unified multi-domain and multi-task training framework that models heterogeneous event schemas within a single model, enabling dynamic adaptation to dataset-specific schemas without requiring complete event label sets at inference time.
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.
Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative sequence-to-sequence framework that performs event extraction subtasks jointly and supports both pipeline and end-to-end configurations. We fine-tune pretrained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large and evolving label spaces. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports document upload, schema-aware event extraction, visualization of triggers and arguments, and comparison of different extraction configurations across domains.
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks. To address these limitations, we introduce MiGUE-Bench, a systematic benchmark for assessing the performance of LLMs in multi-granularity event analysis. To support large-scale evaluation, we first develop an LLM-driven self-correcting annotation framework called MiGUE-Pipeline, enabling scalable acquisition of high-quality source data of events with automatic labels. Then, we design four core tasks in our benchmark, i.e., event detection, relation reasoning, structure induction, and future prediction, to probe model competence at different levels, from atomic event details to complex cross-document narratives. Extensive experiments on state-of-the-art LLMs and retrieval-augmented generation (RAG) methods delineate the current capability boundary and identify critical deficiencies, providing insights into the the future improvement of LLMs in challenging event analysis tasks.
Tao Wen, Shuai Shao, Pei Ke et al.· Annual International ACM SIG...· 0 citations
Natural Language Understanding (NLU) addresses cross-domain challenges in understanding language due to inconsistent data distributions, uncertain concept semantics, and inconsistent language use specific to individual domains across different geographical locations. The majority of classical deep learning systems perform poorly because it cannot generalize, leading to cross-domain underperformance in practical settings. Therefore, this research develops a new class of meta-learning that provides a systematic means to advance cross-domain adaptability for NLU in the context of distributed knowledge systems. More specifically, this approach combines federated learning methods, model-agnostic meta-learning (MAML), and domain-adaptive transformers for the first time to enable rapid extraction of domain-invariant representations while concurrently maintaining local contextual semantics across reasonably distributed datasets. The author conducted benchmark assessments on the most recent multi-domain evaluations and cross-lingual and domain-shifted data to simulate distributed knowledge systems. The custom model outperformed all baselines, transformers, and fine-tuning models by an average of 12.8% cross-domain accuracy, 15.3% lower generalization error, and 18.6% faster convergence rate. In the scenario, the new model decreased to less than 90% and maintained over 90% of its performance after a 40% reduction in training samples. Meta-learning enables superior transfer efficiency and model adaptation to new, previously unseen domains with minimal training. The new meta-learning frameworks successfully addressed the required performance and adaptability for advanced intelligent information systems for cross-domain NLU in distributed knowledge bases.
Deepti Patnaik, Rakshak Bharti· 2026 6th International Confe...· 0 citations
FITTER consistently outperforms inductive baselines without retraining, indicating that vocabulary-agnostic structural learning is a viable foundation for inference over the heterogeneous knowledge graphs of the Semantic Web.
Jiaxin Pan, M. Nayyeri, Osama Mohammed et al.· 0 citations
Data-limited multi-domain machine translation remains challenging because parallel corpora are scarce in specialized domains, domain terminology is highly constrained, and large language models may generate fluent but unfaithful translations. Direct prompting or ordinary fine-tuning is therefore insufficient for domain-sensitive translation scenarios. To address these issues, this paper proposes DKA-LLM-MT, a large-language-model-enhanced adaptive optimization framework for data-limited multi-domain machine translation. The framework follows a data–model–reliability design. First, a domain-knowledge-constrained data augmentation strategy generates pseudo-parallel corpora under terminology, semantic consistency, and domain-style constraints. Second, a retrieval-augmented parameter-efficient adaptation mechanism integrates domain memory retrieval, lightweight LoRA adapters, and dynamic domain routing. Third, a reliability-aware optimization mechanism incorporates semantic fidelity, terminology consistency, and hallucination risk into both training-time data selection and inference-time candidate reranking. Experiments are conducted on five public data-limited domain translation benchmarks covering medical, legal, technical, news, and spoken-style texts. The proposed method achieves an average BLEU of 36.18, chrF of 62.14, COMET of 0.816, and TER of 40.62, consistently outperforming strong neural, multilingual, and LLM-based baselines. Additional matched-backbone and same-language-pair analyses are included to separate the effect of domain adaptation from language-pair variation. Reliability evaluation further shows that DKA-LLM-MT improves terminology accuracy to 89.6% and reduces hallucination rate to 3.2%. The proposed framework provides an effective and reliable solution for domain-sensitive machine translation under limited bilingual supervision and offers practical support for bilingual reading, specialized translation assistance, and domain-oriented language learning.
This work proposes a novel framework, DOMINO, that learns a minimal sufficient domain representation from reference samples and leverages it to guide the generation of domain-aligned synthetic data, enabling practical and scalable domain adaptation without manual prompt design or natural language domain specifications.
Tong Ye, Hang Yu, Tengfei Ma et al.· Proceedings of the 32nd ACM...· 1 citation