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Simplifying Neural Machine Translation with Addition Subtraction Twin Gated Recurrent Networks

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Figure 1ht−1: Architecture for LSTM, GRU and ATR.×σtanhht×σ×1-+ht−1×+htσ- c∗ indicates the memory cell specific to the LSTMNeural NetworkNeural NetworkConcatenateCopyLayerPointwiseOperationVectornetwork.Transfer x∗ and h∗ denote the input and output hidden states respectively.
Table 1: Comparison of LSTM, GRU, RAN andATR in terms of the number of weight matrices(WM) and matrix transformations (MT).
Table 2: Tokenized (encoder (architecture proposed byaverage attention network proposed bytok) and detokenized (detok) case-sensitive BLEU scores on the WMT14 English-German translation task
Table 3: Tokenized (tok) and detokenized (detok) case-sensitive BLEU scores on the WMT14 English-French translation task
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