Dynamic contextualized word embeddings

Conference item

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic context. Based on a pretrained language model (PLM), dynamic contextualized word embeddings model time and social space jointly, which makes them attractive for a range of NLP tasks involving semantic variability. We highlight potential application scenarios by means of qualitative and quantitative analyses on four English datasets.

Authors: Hofmann, V; Pierrehumbert, JB; Schütze, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Association for Computational Linguistics
• Host title: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
• Pages: 6970-6984
• Publication date: 2021-08-01
• Acceptance date: 2021-05-05
• Event title: Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)
• Event location: Bangkok, Thailand
• Event website: https://2021.aclweb.org/
• Event start date: 2021-08-01
• Event end date: 2021-08-06
• DOI: 10.18653/v1/2021.acl-long.542
• ISBN: 9781954085527
• Language: English
• Keywords: FFR