Temporal Relation Classification
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Benchmarks
Most implemented
tieval: An Evaluation Framework for Temporal Information Extraction Systems
How about Time? Probing a Multilingual Language Model for Temporal Relations
Selecting Optimal Context Sentences for Event-Event Relation Extraction
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Papers
Looking for the Bottleneck in Fine-grained Temporal Relation Classification
Temporal relation classification is the task of determining the temporal relation between pairs of temporal entities in a text. Despite recent advancements in natural language processing, temporal relation classification…
Temporal Relation ClassificationWill LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown promising performance in temporal reasonin…
In-Context LearningQuestion AnsweringRelationRelation Classification+2Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time, and document creation time (DCT). It leads to two crucial limits: 1) Tw…
Multi-Task LearningRelationRelation ClassificationTemporal Relation Classification+1tieval: An Evaluation Framework for Temporal Information Extraction Systems
Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volum…
Event DetectionEvent ExtractionTemporal Information ExtractionTemporal Relation Classification+2Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct…
counterfactualRelationRelation ExtractionTemporal Relation Classification+1Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning
“Event Temporal Relation Classification (ETRC) is crucial to natural language understanding. In recent years, the mainstream ETRC methods may not take advantage of lots of semantic information contained in golden tempora…
Contrastive LearningData AugmentationLanguage ModelingLanguage Modelling+4