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SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings
Engler, Jan
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Sikdar, Sandipan
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Lutz, Marlene
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Strohmaier, Markus
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DOI:
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https://doi.org/10.48550/ARXIV.2301.04704
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URL:
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https://aclanthology.org/2022.findings-emnlp.338
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2023
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Buchtitel:
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing : EMNLP 2022
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Seitenbereich:
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4607-4619
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Veranstaltungstitel:
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EMNLP 2022
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Veranstaltungsort:
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Abu Dhabi, United Arab Emirates
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Veranstaltungsdatum:
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07.-11.12.2022
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Herausgeber:
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Goldberg, Yoav
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Kozareva, Zornitsa
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Zhang, Yue
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Ort der Veröffentlichung:
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Stroudsburg, PA
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Verlag:
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ACL
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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Betriebswirtschaftslehre > Data Science in the Economic and Social Sciences (Strohmaier, 2022-)
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Fachgebiet:
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400 Sprache, Linguistik
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Abstract:
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Adding interpretability to word embeddings represents an area of active research in text representation. Recent work has explored thepotential of embedding words via so-called polar dimensions (e.g. good vs. bad, correct vs. wrong). Examples of such recent approaches include SemAxis, POLAR, FrameAxis, and BiImp. Although these approaches provide interpretable dimensions for words, they have not been designed to deal with polysemy, i.e. they can not easily distinguish between different senses of words. To address this limitation, we present SensePOLAR, an extension of the original POLAR framework that enables word-sense aware interpretability for pre-trained contextual word embeddings. The resulting interpretable word embeddings achieve a level of performance that is comparable to original contextual word embeddings across a variety of natural language processing tasks including the GLUE and SQuAD benchmarks. Our work removes a fundamental limitation of existing approaches by offering users sense aware interpretations for contextual word embeddings.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Engler, Jan
;
Sikdar, Sandipan
;
Lutz, Marlene
;
Strohmaier, Markus
Google Scholar:
Engler, Jan
;
Sikdar, Sandipan
;
Lutz, Marlene
;
Strohmaier, Markus
ORCID:
Engler, Jan, Sikdar, Sandipan, Lutz, Marlene  ORCID: https://orcid.org/0000-0002-8265-2410 and Strohmaier, Markus ORCID: https://orcid.org/0000-0002-5485-5720
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