Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings-Reference-Cited by-同舟云学术

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Lost in Context? On the Sense-Wise Variance of Contextualized Word Embeddings

Published:2024 Issue: Volume:32 Page:639-650
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Wang Yile¹^ORCID,Zhang Yue²^ORCID

Affiliation:

1. Institute for AI Industry Research, Tsinghua University, Beijing, China

2. School of Engineering, Westlake University, Hangzhou, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/10304349/10333999.pdf?arnumber=10333999

Reference65 articles.

1. Deep Contextualized Word Representations

2. Language models are unsupervised multitask learners;Radford;OpenAI blog,2019

3. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin,2019

4. RoBERTa: A robustly optimized bert pretraining approach;Liu,2019

5. XLNet: Generalized autoregressive pretraining for language understanding;Yang,2019

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