SOTAVerified

Temporal Relation Extraction

Temporal relation extraction systems aim to identify and classify the temporal relation between a pair of entities provided in a text. For instance, in the sentence "Bob sent a message to Alice while she was leaving her birthday party." one can infer that the actions "sent" and "leaving" entails a temporal relation that can be described as "simultaneous".

Papers

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Showing 21–30 of 88 papers

TitleStatusHype
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs—0
An Improved Neural Baseline for Temporal Relation Extraction—0
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain—0
Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative—0
Beyond Pairwise: Global Zero-shot Temporal Graph Generation—0
Exploring Text Representations for Generative Temporal Relation Extraction—0
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction—0
Event Temporal Relation Extraction with Bayesian Translational Model—0
Attention Neural Model for Temporal Relation Extraction—0
A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-4o (CoT)Text Score59.2—Unverified
2GPT-4oText Score54—Unverified
3Qwen2-VL-72BText Score50.4—Unverified
4LLaVA-OneVision-Qwen2-72BText Score48.4—Unverified
5LLaVA-OneVision-Qwen2-7BText Score41.6—Unverified
6Qwen2-VL-7BText Score40.2—Unverified
7Gemini-1.5-Pro (CoT)Text Score37—Unverified
8VideoLLaMA2-72BText Score36.2—Unverified
9Gemini-1.5-ProText Score35.8—Unverified
10Claude 3.5 SonnetText Score32.8—Unverified