Browse State-of-the-Art › Temporal Relation Extraction
Temporal Relation Extraction
32 papers with code · 1 benchmark · 4 datasets archive 2025-07-28
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".
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| Vinoground (24 rows) | GPT-4o (CoT) | — | — | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
4 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 32 papers with code (88 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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26 Feb 2021 82 repositories listed Syntology ran 16 of 20 samples · 4 unverified · 16 pointer-only (licence)State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
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26 May 2022 10 repositories listed Syntology ran 13 of 19 samples · 6 unverified · 13 pointer-only (licence)Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task.
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18 Sep 2024 8 repositories listed Syntology ran 8 of 12 samples · 4 unverifiedWe present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing.
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16 Nov 2023 6 repositories listed Syntology ran 4 of 7 samples · 3 unverified · 1 pointer-only (licence)In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
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3 Oct 2023 6 repositories listed Syntology ran 7 of 14 samples · 7 unverifiedWe thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
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11 Jun 2024 3 repositories listed Syntology ran 7 of 17 samples · 10 unverifiedIn this paper, we present the VideoLLaMA 2, a set of Video Large Language Models (Video-LLMs) designed to enhance spatial-temporal modeling and audio understanding in video and audio-oriented tasks.
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9 May 2023 3 repositories listed Syntology ran 24 of 34 samples · 10 unverified · 32 pointer-only (licence)We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together.
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6 Aug 2024 2 repositories listedWe present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series.
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3 Aug 2024 2 repositories listed Syntology ran 9 of 14 samples · 5 unverifiedThe recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone.
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28 Sep 2021 2 repositories listedWe present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks.
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16 Dec 2020 2 repositories listedThere has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
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24 Oct 2020 2 repositories listedA principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more.
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22 Jun 2012 2 repositories listedWe describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise.
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3 Jul 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)This long-context capability allows IXC-2.
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17 Jun 2024 1 repository listedOur experiments demonstrate that LLMs struggle in the zero-shot setting performing worse than fine-tuned specialized models in terms of F1 score, showing that this is a challenging task for LLMs.
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8 Apr 2024 1 repository listed Syntology ran 7 of 9 samples · 2 unverifiedHowever, existing LLM-based large multimodal models (e.
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8 Mar 2024 1 repository listedIn this report, we introduce the Gemini 1.
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30 Nov 2023 1 repository listed Syntology ran 5 of 11 samples · 6 unverified · 11 pointer-only (licence)Large language models (LLMs) have shown remarkable text understanding capabilities, which have been extended as Video LLMs to handle video data for comprehending visual details.
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26 Oct 2023 1 repository listedTemporal relation extraction models have thus far been hindered by a number of issues in existing temporal relation-annotated news datasets, including: (1) low inter-annotator agreement due to the lack of specificity of…
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2 Oct 2023 1 repository listedIn this paper, we introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context which requires multiple reasoning over multiple events, and…
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2 Apr 2023 1 repository listedTemporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events.
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11 Jan 2023 1 repository listedAll in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems.
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2 Apr 2022 1 repository listedTo achieve this goal, our work addresses the problems of subevent relation extraction (SRE) and temporal event relation extraction (TRE) that aim to predict subevent and temporal relations between two given event…
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1 Nov 2021 1 repository listedEvent time is one of the most important features for event-event temporal relation extraction.
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12 Sep 2021 1 repository listedRecent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.
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19 Apr 2021 1 repository listedExtracting temporal relations (e.
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13 Jan 2021 1 repository listedWe present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration…
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15 Sep 2020 1 repository listedExtracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding.
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22 Sep 2019 1 repository listedWe propose a novel deep structured learning framework for event temporal relation extraction.
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7 Aug 2018 1 repository listedIn this work, we extend our classification model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model.
Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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