Abstract
Word embeddings are useful for a wide vari- ety of tasks, but they lack interpretability. By rotating word spaces, interpretable dimensions can be identified while preserving the informa- tion contained in the embeddings without any loss. In this work, we investigate three meth- ods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we pro- pose. In contrast to Densifier, DensRay can be computed in closed form, is hyperparameter- free and thus more robust than Densifier. We evaluate the three methods on lexicon induc- tion and set-based word analogy. In addition we provide qualitative insights as to how inter- pretable word spaces can be used for removing gender bias from embeddings.
Dokumententyp: | Konferenz |
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EU Funded Grant Agreement Number: | 740516 |
EU-Projekte: | Horizon 2020 > ERC Grants > ERC Advanced Grant > ERC Grant 740516: NonSequeToR - Non-sequence models for tokenization replacement |
Fakultätsübergreifende Einrichtungen: | Centrum für Informations- und Sprachverarbeitung (CIS) |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
400 Sprache > 410 Linguistik |
URN: | urn:nbn:de:bvb:19-epub-72192-5 |
ISBN: | 978-1-950737-90-1 |
Ort: | Stroudsburg, USA |
Sprache: | Englisch |
Dokumenten ID: | 72192 |
Datum der Veröffentlichung auf Open Access LMU: | 20. Mai 2020, 09:48 |
Letzte Änderungen: | 04. Nov. 2020, 13:53 |