Papers with temporal relation classification

6 papers
Extracting Temporal Event Relation with Syntax-guided Graph Transformer (2022.findings-naacl)

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Challenge: Temporal relationship extraction is crucial for understanding complex events and reasoning over them.
Approach: They propose a Syntax-guided Graph Transformer network to extract temporal relations between events by explicitly exploiting the connection between two events based on their dependency parsing trees.
Outcome: The proposed approach outperforms state-of-the-art methods on MATRES and TB-DENSE with up to 7.9% absolute F-score gain.
Temporal Event Knowledge Acquisition via Identifying Narratives (P18-1)

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Challenge: Existing knowledge of narrative examples is lacking and difficult to obtain.
Approach: They propose a weakly supervised approach for acquiring rich temporal event knowledge across sentences in narrative stories.
Outcome: The proposed approach outperforms neural network models on the narrative cloze task.
Systems’ Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the TempEval-3 Task (L18-1)

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Challenge: Temporal Processing systems are crucial for timelines and storylines . TempEval-3 is the latest evaluation campaign on open-domain TP in english .
Approach: They present a Temporal Processing system that incorporates high level lexical semantic features and uses them to evaluate temporal relation classification.
Outcome: The proposed system achieves the best scores for event detection and temporal relation classification from raw text, but the errors are not as robust as previous systems.
Temporal Relation Classification using Boolean Question Answering (2023.findings-acl)

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Challenge: a new approach for temporal relation classification (TRC) is proposed . a boolean question answering model is used to classify temporal relations between two events .
Approach: They propose an efficient approach for temporal relation classification using a boolean question answering model based on TRC annotation guidelines.
Outcome: The proposed model outperforms state-of-the-art models by 2.4% on questions designed by human annotation experts.
How about Time? Probing a Multilingual Language Model for Temporal Relations (2022.coling-1)

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Challenge: XLM-R is a multilingual language model for temporal relation classification between events in four languages.
Approach: They propose to use a multilingual language model for temporal relation classification between events in four languages to obtain contextualized embeddings.
Outcome: The proposed model outperforms state-of-the-art models in obtaining competitive results against state- of-the art systems, but lacks suitable encoded information to address this task.
Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (2021.emnlp-main)

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Challenge: Existing methods for event-event temporal relation extraction are sparse on event-time information.
Approach: They propose a model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers.
Outcome: The proposed model significantly improves the RoBERTa-based baseline and achieves state-of-the-art performance on MATRES dataset.

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