MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction

• URL: http://arxiv.org/abs/2211.07342v1
• Date: Mon, 14 Nov 2022 13:34:49 GMT
• Title: MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
• Authors: Xiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng, Zimu Wang, Yankai Lin, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Peng Li, Jie Zhou
• Abstract summary: We construct a large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103,193 event coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and 15,841 subevent relations. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances.
• Score: 78.61546292830081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages. However, two drawbacks of existing datasets limit event relation extraction (ERE) tasks: (1) Small scale. Due to the annotation complexity, the data scale of existing datasets is limited, which cannot well train and evaluate data-hungry models. (2) Absence of unified annotation. Different types of event relations naturally interact with each other, but existing datasets only cover limited relation types at once, which prevents models from taking full advantage of relation interactions. To address these issues, we construct a unified large-scale human-annotated ERE dataset MAVEN-ERE with improved annotation schemes. It contains 103,193 event coreference chains, 1,216,217 temporal relations, 57,992 causal relations, and 15,841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude. Experiments show that ERE on MAVEN-ERE is quite challenging, and considering relation interactions with joint learning can improve performances. The dataset and source codes can be obtained from https://github.com/THU-KEG/MAVEN-ERE.

Related papers

• Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning [35.27714529976667]
  Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (Tlink) between two mentions. This paper presents an event centric model that allows to manage dynamic event representations across multiple Tlink categories. Our proposal outperforms state-of-the-art models and two transfer learning baselines on both the English and Japanese data.
  arXiv  Detail & Related papers  (2023-10-31T07:41:24Z)
• More than Classification: A Unified Framework for Event Temporal Relation Extraction [61.44799147458621]
  Event temporal relation extraction(ETRE) is usually formulated as a multi-label classification task. We observe that all relations can be interpreted using the start and end time points of events. We propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points.
  arXiv  Detail & Related papers  (2023-05-28T02:09:08Z)
• Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization [41.44022912961265]
  We propose a novel Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence relationship between actions. Besides the visual information, it further utilizes the semantic embeddings of class labels to model the co-occurrence relationship. Our method achieves high multi-label relationship modeling capacity.
  arXiv  Detail & Related papers  (2023-03-15T09:07:04Z)
• RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction [16.87868728956481]
  We propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE) To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance.
  arXiv  Detail & Related papers  (2022-06-07T15:11:42Z)
• Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction [84.64435075778988]
  We propose a general approach to learn relation prototypes from unlabeled texts. We learn relation prototypes as an implicit factor between entities. We conduct experiments on two publicly available datasets: New York Times and Google Distant Supervision.
  arXiv  Detail & Related papers  (2020-11-27T06:21:12Z)
• Joint Constrained Learning for Event-Event Relation Extraction [94.3499255880101]
  We propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data.
  arXiv  Detail & Related papers  (2020-10-13T22:45:28Z)
• Type-augmented Relation Prediction in Knowledge Graphs [65.88395564516115]
  We propose a type-augmented relation prediction (TaRP) method, where we apply both the type information and instance-level information for relation prediction. Our proposed TaRP method achieves significantly better performance than state-of-the-art methods on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-16T21:14:18Z)
• Relation of the Relations: A New Paradigm of the Relation Extraction Problem [52.21210549224131]
  We propose a new paradigm of Relation Extraction (RE) that considers as a whole the predictions of all relations in the same context. We develop a data-driven approach that does not require hand-crafted rules but learns by itself the relation of relations (RoR) using Graph Neural Networks and a relation matrix transformer. Experiments show that our model outperforms the state-of-the-art approaches by +1.12% on the ACE05 dataset and +2.55% on SemEval 2018 Task 7.2.
  arXiv  Detail & Related papers  (2020-06-05T22:25:27Z)
• On Embeddings in Relational Databases [11.52782249184251]
  We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Recent methods for learning embedding constitute of a naive approach to consider complete denormalization of the database by relationalizing the full join of all tables and representing as a knowledge graph. In this paper we demonstrate; a better methodology for learning representations by exploiting the underlying semantics of columns in a table while using the relation joins and the latent inter-row relationships.
  arXiv  Detail & Related papers  (2020-05-13T17:21:27Z)

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.