@inproceedings{schlechtweg-etal-2026-lscd,
title = "The {LSCD} Benchmark: a Testbed for Diachronic Word Meaning Tasks",
author = "Schlechtweg, Dominik and
Yadav, Sachin and
Kuhn, Jonas and
Arefyev, Nikolay",
editor = "Mohammad, Saif M. and
Ousidhoum, Nedjma",
booktitle = "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*{SEM} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.starsem-conference.10/",
pages = "148--163",
ISBN = "979-8-89176-413-2",
abstract = "Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task{'}s modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art."
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<abstract>Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task’s modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art.</abstract>
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%0 Conference Proceedings
%T The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks
%A Schlechtweg, Dominik
%A Yadav, Sachin
%A Kuhn, Jonas
%A Arefyev, Nikolay
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F schlechtweg-etal-2026-lscd
%X Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task’s modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art.
%U https://aclanthology.org/2026.starsem-conference.10/
%P 148-163
Markdown (Informal)
[The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://aclanthology.org/2026.starsem-conference.10/) (Schlechtweg et al., *SEM 2026)
ACL
- Dominik Schlechtweg, Sachin Yadav, Jonas Kuhn, and Nikolay Arefyev. 2026. The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 148–163, San Diego, California, United States. Association for Computational Linguistics.