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Analysis of Affine Transformations in BERT Semantical Space

Student: Andrei Solodiankin

Supervisor: Eduard Klyshinskiy

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Information Science and Computation Technology (Bachelor)

Year of Graduation: 2021

Modern methods of natural language processing (NLP) are based on models of vector representation of words. One of the features of these models is to solve the problem of proportional analogy. This problem has been repeatedly investigated for non contextualized models. In this paper, we investigate affine transformations of parallel transport for contextualized models. Numerical experiments were carried out with vector representations of the ELMo and BERT models, which showed that parallel transfer does not always make it possible to obtain the correct solution.

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