Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Machine Translation with Noisy Lexical Constraints   Lexically constrained decoding for machine translation has shown to be
beneficial in previous studies. Unfortunately, constraints provided by users
may contain mistakes in real-world situations. It is still an open question
that how to manipulate these noisy constraints in such practical scenarios. We
present a novel framework that treats constraints as external memories. In this
soft manner, a mistaken constraint can be corrected. Experiments demonstrate
that our approach can achieve substantial BLEU gains in handling noisy
constraints. These results motivate us to apply the proposed approach on a new
scenario where constraints are generated without the help of users. Experiments
show that our approach can indeed improve the translation quality with the
automatically generated constraints.

22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas