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Analysis of Affine Transformations in Semantic Spaces of Contextualisation Models

Student: Vladimir Kulichenko

Supervisor: Eduard Klyshinskiy

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

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2021

Word embedding models are able to solve various types of NLP tasks. The ability of an embedding model to solve the proportional analogy task is a popular metric of its quality. In this thesis, the proportional analogy task will be solved using affine transformation of the parallel translation in semantic spaces of word vectors, embedded using pretrained BERT and ELMo models. The results of numerical experiments revealed, that this method of solving proportional analogy problem does not always gives a correct result.

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