  In this paper, we propose an additionsubtraction twin-gated recurrent network
(ATR) to simplify neural machine translation. The recurrent units of ATR are
heavily simplified to have the smallest number of weight matrices among units
of all existing gated RNNs. With the simple addition and subtraction operation,
we introduce a twin-gated mechanism to build input and forget gates which are
highly correlated. Despite this simplification, the essential non-linearities
and capability of modeling long-distance dependencies are preserved.
Additionally, the proposed ATR is more transparent than LSTM/GRU due to the
simplification. Forward self-attention can be easily established in ATR, which
makes the proposed network interpretable. Experiments on WMT14 translation
tasks demonstrate that ATR-based neural machine translation can yield
competitive performance on English- German and English-French language pairs in
terms of both translation quality and speed. Further experiments on NIST
Chinese-English translation, natural language inference and Chinese word
segmentation verify the generality and applicability of ATR on different
natural language processing tasks.
