|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
SOTA for Common Sense Reasoning on SWAG
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.
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
First, the majority of datasets for sequential short-text classification (i. e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task.
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
However, as deep learning models require a large amount of training data, applying deep learning to biomedical text mining is often unsuccessful due to the lack of training data in biomedical fields.
We present NMT-Keras, a flexible toolkit for training deep learning models, which puts a particular emphasis on the development of advanced applications of neural machine translation systems, such as interactive-predictive translation protocols and long-term adaptation of the translation system via continuous learning.
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
SOTA for Named Entity Recognition on NCBI-disease (using extra training data)
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.
#10 best model for Question Answering on WikiQA
We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i. e., using nothing but random parameterizations.