(2015) all adopted a different version of the RNN with an LSTM-inspired hidden unit, the gated re-current unit (GRU), for both components.4 In more detail, one can parameterize the proba- The categories depend on the chosen dataset and can range from topics. Image by the author. We have the encoder (blue rectangle) build with an Input Layer and a Recurrent Neural Network (RNN) more precisely a Long Short-Term Memory (LSTM).The encoder receives the Spanish sentence and outputs a single vector which is the hidden state of the last LSTM time step, the meaning of the whole sentence is captured in this vector. Text classification is the task of assigning a sentence or document an appropriate category. a Long Short-Term Memory (LSTM) hidden unit for both the encoder and the decoder. Understanding LSTM Networks (blog post overview) Tue Feb 2: Machine Translation, Attention, Subword Models Suggested Readings: Statistical Machine Translation slides, CS224n 2015 (lectures 2/3/4) Statistical Machine Translation (book by Philipp Koehn) BLEU (original paper) (2014), Bahdanau et al. Bidirectional attention is then applied on graphs and queries to generate a query-aware nodes representation, which will be used for the final prediction. Cho et al. (2015), and Jean et al. Experimental evaluation shows BAG achieves state-of-the-art accuracy performance on the QAngaroo WIKIHOP dataset.
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