@inproceedings{ren-peng-2025-tictac,
title = "{T}ic{T}ac: Time-aware Supervised Fine-tuning for Automatic Text Dating",
author = "Ren, Han and
Peng, Minna",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1129/",
doi = "10.18653/v1/2025.findings-acl.1129",
pages = "21906--21918",
ISBN = "979-8-89176-256-5",
abstract = "Pre-trained langauge models have achieved success in many natural language processing tasks, whereas they are trapped by the time-agnostic setting, impacting the performance in automatic text dating. This paper introduces TicTac, a supervised fine-tuning model for automatic text dating. Unlike the existing models that always ignore the temporal relatedness of documents, TicTac has the ability to learn temporal semantic information, which is helpful for capturing the temporal implications over long-time span corpora. As a fine-tuning framework, TicTac employs a contrastive learning-based approach to model two types of temporal relations of diachronic documents. TicTac also adopts a metric learning approach, where the temporal distance between a historical text and its category label is estimated, which benefits to learn temporal semantic information on texts with temporal ordering. Experiments on two diachronic corpora show that our model effectively captures the temporal semantic information and outperforms state-of-the-art baselines."
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<abstract>Pre-trained langauge models have achieved success in many natural language processing tasks, whereas they are trapped by the time-agnostic setting, impacting the performance in automatic text dating. This paper introduces TicTac, a supervised fine-tuning model for automatic text dating. Unlike the existing models that always ignore the temporal relatedness of documents, TicTac has the ability to learn temporal semantic information, which is helpful for capturing the temporal implications over long-time span corpora. As a fine-tuning framework, TicTac employs a contrastive learning-based approach to model two types of temporal relations of diachronic documents. TicTac also adopts a metric learning approach, where the temporal distance between a historical text and its category label is estimated, which benefits to learn temporal semantic information on texts with temporal ordering. Experiments on two diachronic corpora show that our model effectively captures the temporal semantic information and outperforms state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T TicTac: Time-aware Supervised Fine-tuning for Automatic Text Dating
%A Ren, Han
%A Peng, Minna
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F ren-peng-2025-tictac
%X Pre-trained langauge models have achieved success in many natural language processing tasks, whereas they are trapped by the time-agnostic setting, impacting the performance in automatic text dating. This paper introduces TicTac, a supervised fine-tuning model for automatic text dating. Unlike the existing models that always ignore the temporal relatedness of documents, TicTac has the ability to learn temporal semantic information, which is helpful for capturing the temporal implications over long-time span corpora. As a fine-tuning framework, TicTac employs a contrastive learning-based approach to model two types of temporal relations of diachronic documents. TicTac also adopts a metric learning approach, where the temporal distance between a historical text and its category label is estimated, which benefits to learn temporal semantic information on texts with temporal ordering. Experiments on two diachronic corpora show that our model effectively captures the temporal semantic information and outperforms state-of-the-art baselines.
%R 10.18653/v1/2025.findings-acl.1129
%U https://aclanthology.org/2025.findings-acl.1129/
%U https://doi.org/10.18653/v1/2025.findings-acl.1129
%P 21906-21918
Markdown (Informal)
[TicTac: Time-aware Supervised Fine-tuning for Automatic Text Dating](https://aclanthology.org/2025.findings-acl.1129/) (Ren & Peng, Findings 2025)
ACL