arXiv Computation and Language
Aug 25

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

The paper introduces a unified multi-domain and multi-task generative framework for event extraction that incorporates explicit domain conditioning signals and task-specific prompts. This design allows a single model to adapt dynamically to different event schemas without needing full event label sets during inference, supporting both pipeline and end-to-end extraction. Experiments on various benchmarks show competitive performance, strong cross-domain generalization, and practical scalability while maintaining domain-specific precision.

By Siting Liang, Omar Adjali, Daniel Sonntag
arXiv Computation and Language
Aug 25

A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework

The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.

By Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag
arXiv Computation and Language
Sep 11

CMNIE: An Information Extraction Benchmark for Chinese Military News

CMNIE is a new benchmark for extracting structured information from Chinese military news, covering event triggers, arguments, named entities, and entity relations under a unified schema. The dataset contains 13,000 manually annotated instances with 7 event types, 10 argument roles, 7 entity types, and 8 relation types. Experiments show that current supervised models, zero‑shot LLMs, and fine‑tuned LLMs struggle with relation extraction and exact span matching, highlighting the challenge of joint structured extraction in this domain.

By Yan Yu, Mengna Zhu, Zhenyu Song, Hao Yang, Haiwen Chen, Mao Wang
arXiv Computation and Language
Sep 25

EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards

EAGER is a reinforcement learning framework for generative event extraction that uses fine‑grained verifiable rewards and Schema‑Contrastive Advantage Estimation to address advantage collapse in sparse binary reward settings. The reward design focuses on structural validity, extraction accuracy, groundedness, coverage, over‑generation, and span precision. Across seven benchmark datasets, EAGER outperforms prompting, supervised fine‑tuning, and previous reinforcement learning baselines, achieving a substantial improvement over the strongest prior method.

By Omar Adjali, Siting Liang, Omair Shahzad Bhatti, Daniel Sonntag
Hugging Face Trending Papers
Jul 30

From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

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.

arXiv Computation and Language
Sep 16

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP

SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.

By Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang