Text classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
( Image credit: Text Classification Algorithms: A Survey )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
Ranked #9 on Sentiment Analysis on IMDb
With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost.
Ranked #4 on Reading Comprehension on RACE
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Ranked #1 on Sentiment Analysis on SST-2 Binary classification
COMMON SENSE REASONING COREFERENCE RESOLUTION DOCUMENT SUMMARIZATION LINGUISTIC ACCEPTABILITY MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD SENSE DISAMBIGUATION
With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.
Ranked #1 on Text Classification on IMDb
We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
IMAGE CAPTIONING IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-LABEL CLASSIFICATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING ONE-SHOT LEARNING SENTIMENT ANALYSIS SPEAKER VERIFICATION TEXT CLASSIFICATION TIME SERIES FORECASTING VISUAL QUESTION ANSWERING