{"title":"HSE at TempoWiC: Detecting Meaning Shift in Social Media with Diachronic Language Models","authors":"Elizaveta Tukhtina, Kseniia Kashleva, Svetlana Vydrina","doi":"10.18653/v1/2022.evonlp-1.6","DOIUrl":null,"url":null,"abstract":"This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.","PeriodicalId":158578,"journal":{"name":"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2022.evonlp-1.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.
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