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Transformer-F: A Transformer network with effective methods for learning universal sentence representation
[article]
2021
arXiv
pre-print
The Transformer model is widely used in natural language processing for sentence representation. However, the previous Transformer-based models focus on function words that have limited meaning in most cases and could merely extract high-level semantic abstraction features. In this paper, two approaches are introduced to improve the performance of Transformers. We calculated the attention score by multiplying the part-of-speech weight vector with the correlation coefficient, which helps extract
arXiv:2107.00653v1
fatcat:kj2fsnokzfbblokzh4kshep57y