@inproceedings{liang-etal-2026-bridging,
title = "Bridging the Temporal Gap: A New Benchmark and Dynasty-aware Adaptation Framework for {A}ncient {C}hinese {WSD}",
author = "Liang, Yuzhi and
Zhu, Xinrong and
Ma, Lixiang and
Wan, Zhixiong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1462/",
pages = "33427--33443",
ISBN = "979-8-89176-459-0",
abstract = "Word sense disambiguation (WSD) is typically studied in synchronic settings, where the mapping from contextualized occurrences to sense labels is assumed to be temporally stable. This assumption may not hold for historical texts, in which meanings may emerge, shift, or disappear over time. We study diachronic WSD for Ancient Chinese, a challenging scenario involving long-term semantic evolution and substantial historical variation. We introduce Ancient Chinese Diachronic Word Sense Disambiguation Benchmark (AC-DiaWSD), a large-scale WSD dataset constructed from the Twenty-Four Histories, containing about 24K manually annotated instances across 12 dynasties from the Western Han to the Qing. We further propose DynaSense, a model-agnostic framework that enhances pre-trained language models with contrastive sense learning and dynasty-conditioned representation adaptation. Experiments with multiple PLMs and LLMs show that existing models struggle with diachronic WSD, while DynaSense consistently improves accuracy and F1 across backbones."
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%0 Conference Proceedings
%T Bridging the Temporal Gap: A New Benchmark and Dynasty-aware Adaptation Framework for Ancient Chinese WSD
%A Liang, Yuzhi
%A Zhu, Xinrong
%A Ma, Lixiang
%A Wan, Zhixiong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liang-etal-2026-bridging
%X Word sense disambiguation (WSD) is typically studied in synchronic settings, where the mapping from contextualized occurrences to sense labels is assumed to be temporally stable. This assumption may not hold for historical texts, in which meanings may emerge, shift, or disappear over time. We study diachronic WSD for Ancient Chinese, a challenging scenario involving long-term semantic evolution and substantial historical variation. We introduce Ancient Chinese Diachronic Word Sense Disambiguation Benchmark (AC-DiaWSD), a large-scale WSD dataset constructed from the Twenty-Four Histories, containing about 24K manually annotated instances across 12 dynasties from the Western Han to the Qing. We further propose DynaSense, a model-agnostic framework that enhances pre-trained language models with contrastive sense learning and dynasty-conditioned representation adaptation. Experiments with multiple PLMs and LLMs show that existing models struggle with diachronic WSD, while DynaSense consistently improves accuracy and F1 across backbones.
%U https://aclanthology.org/2026.emnlp-main.1462/
%P 33427-33443
Markdown (Informal)
[Bridging the Temporal Gap: A New Benchmark and Dynasty-aware Adaptation Framework for Ancient Chinese WSD](https://aclanthology.org/2026.emnlp-main.1462/) (Liang et al., EMNLP 2026)
ACL