  Neural machine translation (NMT) heavily relies on word-level modelling to
learn semantic representations of input sentences. However, for languages
without natural word delimiters (e.g., Chinese) where input sentences have to
be tokenized first, conventional NMT is confronted with two issues: 1) it is
difficult to find an optimal tokenization granularity for source sentence
modelling, and 2) errors in 1-best tokenizations may propagate to the encoder
of NMT. To handle these issues, we propose word-lattice based Recurrent Neural
Network (RNN) encoders for NMT, which generalize the standard RNN to word
lattice topology. The proposed encoders take as input a word lattice that
compactly encodes multiple tokenizations, and learn to generate new hidden
states from arbitrarily many inputs and hidden states in preceding time steps.
As such, the word-lattice based encoders not only alleviate the negative impact
of tokenization errors but also are more expressive and flexible to embed input
sentences. Experiment results on Chinese-English translation demonstrate the
superiorities of the proposed encoders over the conventional encoder.
