mlsanityData to 7 Oct 2026

TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

arXiv 2209.07216 ↗First posted 15 Sep 2022cs.CLcited by 19

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Abstract (arXiv)

Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 20 of its 36 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

20 of 36 references.

References · 20

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  3. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  4. 2018WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representationscited by 370
  5. 2020BERTweet: A pre-trained language model for English Tweetscited by 346
  6. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 290
  7. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  8. 2022TimeLMs: Diachronic Language Models from Twittercited by 129
  9. 2021Lifelong Pretraining: Continually Adapting Language Models to Emerging Corporacited by 114
  10. 2021Time Waits for No One! Analysis and Challenges of Temporal Misalignmentcited by 91
  11. 2021Time Masking for Temporal Language Modelscited by 46
  12. 2021Temporal Effects on Pre-trained Models for Language Processing Taskscited by 46
8 more
  1. 2020XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualizationcited by 42
  2. 2018Short-Term Meaning Shift: A Distributional Explorationcited by 23
  3. 2020Dynamic Contextualized Word Embeddingscited by 20
  4. 2021LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyondcited by 17
  5. 2021AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examplescited by 14
  6. 2020WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Contextcited by 13
  7. 2021Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crispcited by 7
  8. 2018Exploring Emoji Usage and Prediction Through a Temporal Variation Lenscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 6; 2025: 2; 2026: 2.

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  1. 2023CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotationcited by 93
  2. 2023A Survey on Contextualised Semantic Shift Detectioncited by 21
  3. 2024A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Changecited by 21
  4. 2023SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Researchcited by 9
  5. 2026Evaluating Decision Models for Text Annotation in Computational Social Sciencecited by 8
  6. 2024(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detectioncited by 7
  7. 2023Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twittercited by 3
  8. 2024Meaning at the Planck scale? Contextualized word embeddings for doing history, philosophy, and sociology of sciencecited by 3
  9. 2025CUB: Benchmarking Context Utilisation Techniques for Language Modelscited by 3
  10. 2022Is It Worth the (Environmental) Cost? Limited Evidence for Temporal Adaptation via Continuous Trainingcited by 2
  11. 2022Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiCcited by 2
  12. 2023Large Language Models on Lexical Semantic Change Detection: An Evaluationcited by 2
7 more
  1. 2024The LSCD Benchmark: a Testbed for Diachronic Word Meaning Taskscited by 2
  2. 2022Temporal Word Meaning Disambiguation using TimeLMscited by 1
  3. 2024CHEW: A Dataset of CHanging Events in Wikipediacited by 1
  4. 2025LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Datacited by 1
  5. 2022Using Two Losses and Two Datasets Simultaneously to Improve TempoWiC Accuracycited by 0
  6. 2024A Systematic Analysis on the Temporal Generalization of Language Models in Social Mediacited by 0
  7. 2026Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detectioncited by 0