The science of asking and answering in natural language with a machine.
A new language representation model which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers.
Bi-Directional Attention Flow (BIDAF) network is a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.
DrQA is a system for reading comprehension applied to open-domain question answering.
A Q&A architecture does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions.
An end-to-end neural networks model for reading comprehension style question answering, which aims to answer questions from a given passage.
R-NET re-implementation in Keras.
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Applied to the SQuAD1.0 dataset. The SQuAD2.0 dataset has released. but DrQA is not tested yet.
Has state-of-the-arts performance.
Is a freely-available semantic network, designed to help computers understand the meanings of words that people use.