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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
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.
Ranked #19 on Natural Language Inference on SNLI
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #1 on Named Entity Recognition on NCBI-disease
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
Ranked #1 on Relation Extraction on JNLPBA
CITATION INTENT CLASSIFICATION DEPENDENCY PARSING LANGUAGE MODELLING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION
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.
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on Chinese Sentence Pair Classification on XNLI (Accuracy metric)
CHINESE DEPENDENCY PARSING CHINESE NAMED ENTITY RECOGNITION CHINESE PART-OF-SPEECH TAGGING CHINESE SEMANTIC ROLE LABELING CHINESE SENTENCE PAIR CLASSIFICATION CHINESE WORD SEGMENTATION DEPENDENCY PARSING DOCUMENT CLASSIFICATION IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING SEMANTIC TEXTUAL SIMILARITY SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders.
Ranked #11 on Question Answering on WikiQA