Abstract
BERT achieves remarkable results in text classification tasks, it is yet not fully exploited, since only the last layer is used as a representation output for downstream classifiers. The most recent studies on the nature of linguistic features learned by BERT, suggest that different layers focus on different kinds of linguistic features. We propose a CNN-Enhanced Transformer-Encoder model which is trained on top of fixed BERT $[CLS]$ representations from all layers, employing Convolutional Neural Networks to generate QKV feature maps inside the Transformer-Encoder, instead of linear projections of the input into the embedding space. CNN-Trans-Enc is relatively small as a downstream classifier and doesn't require any fine-tuning of BERT, as it ensures an optimal use of the $[CLS]$ representations from all layers, leveraging different linguistic features with more meaningful, and generalizable QKV representations of the input. Using BERT with CNN-Trans-Enc keeps $98.9\%$ and $94.8\%$ of current state-of-the-art performance on the IMDB and SST-5 datasets respectably, while obtaining new state-of-the-art on YELP-5 with $82.23$ ($8.9\%$ improvement), and on Amazon-Polarity with $0.98\%$ ($0.2\%$ improvement) (K-fold Cross Validation on a 1M sample subset from both datasets). On the AG news dataset CNN-Trans-Enc achieves $99.94\%$ of the current state-of-the-art, and achieves a new top performance with an average accuracy of $99.51\%$ on DBPedia-14. Index terms: Text Classification, Natural Language Processing, Convolutional Neural Networks, Transformers, BERT
Abstract (translated)
URL
https://arxiv.org/abs/2209.06344
