Sentiment analysis is the task of classifying the polarity of a given text.
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Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting.
#8 best model for Sentiment Analysis on IMDb
Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data.
#9 best model for Sentiment Analysis on IMDb
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
#3 best model for Sentiment Analysis on SST-5 Fine-grained classification
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
#12 best model for Sentiment Analysis on Yelp Fine-grained classification
Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing.
#2 best model for Semantic Textual Similarity on STS Benchmark