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Found 3 papers, 1 papers with code
CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Sampling Informative Training Data for RNN Language Models
We propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNNs) language models.
VecShare: A Framework for Sharing Word Representation Vectors
Many Natural Language Processing (NLP) models rely on distributed vector representations of words.
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