  This paper presents a novel fusion method for integrating an external
language model (LM) into the Transformer based sequence-to-sequence (seq2seq)
model. While paired data are basically required to train the seq2seq model, the
external LM can be trained with only unpaired data. Thus, it is important to
leverage memorized knowledge in the external LM for building the seq2seq model,
since it is hard to prepare a large amount of paired data. However, the
existing fusion methods assume that the LM is integrated with recurrent neural
network-based seq2seq models instead of the Transformer. Therefore, this paper
proposes a fusion method that can explicitly utilize network structures in the
Transformer. The proposed method, called {\bf memory attentive fusion},
leverages the Transformer-style attention mechanism that repeats source-target
attention in a multi-hop manner for reading the memorized knowledge in the LM.
Our experiments on two text-style conversion tasks demonstrate that the
proposed method performs better than conventional fusion methods.
