  In Neural Machine Translation (NMT), the decoder can capture the features of
the entire prediction history with neural connections and representations. This
means that partial hypotheses with different prefixes will be regarded
differently no matter how similar they are. However, this might be inefficient
since some partial hypotheses can contain only local differences that will not
influence future predictions. In this work, we introduce recombination in NMT
decoding based on the concept of the "equivalence" of partial hypotheses.
Heuristically, we use a simple $n$-gram suffix based equivalence function and
adapt it into beam search decoding. Through experiments on large-scale
Chinese-to-English and English-to-Germen translation tasks, we show that the
proposed method can obtain similar translation quality with a smaller beam
size, making NMT decoding more efficient.
