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.
#3 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.
#4 best model for Sentiment Analysis on IMDb
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).
#2 best model for Coreference Resolution on CoNLL 2012
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.
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
DOMAIN ADAPTATION MACHINE TRANSLATION NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING RELATION EXTRACTION SEMANTIC PARSING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
SOTA for Text Classification on TREC-6