Looking for modern algorithms using NN Language Model implementations addressing polysemy in NLP tasks, including text classification, question answering and topic modeling. Transfer/Zero-short learning methods are most interesting to find. Any working solutions with BERT and Hugging Face Transformers libraries?
Topic question-answering classification topic-model nlp
Category Data Science
Maybe this article will help you How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.
Talks about contextual word embeddings like BERT and GPT how they can capture various polysemous concepts rather than the static word embeddings which create a single representation for each word, such as GloVe.
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