Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting and Understanding Generalization Barriers for Neural Machine
  Translation   Generalization to unseen instances is our eternal pursuit for all data-driven
models. However, for realistic task like machine translation, the traditional
approach measuring generalization in an average sense provides poor
understanding for the fine-grained generalization ability. As a remedy, this
paper attempts to identify and understand generalization barrier words within
an unseen input sentence that \textit{cause} the degradation of fine-grained
generalization. We propose a principled definition of generalization barrier
words and a modified version which is tractable in computation. Based on the
modified one, we propose three simple methods for barrier detection by the
search-aware risk estimation through counterfactual generation. We then conduct
extensive analyses on those detected generalization barrier words on both
Zh$\Leftrightarrow$En NIST benchmarks from various perspectives. Potential
usage of the detected barrier words is also discussed.

29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int