Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Memory Attentive Fusion: External Language Model Integration for
  Transformer-based Sequence-to-Sequence Model   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.

17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc