Machine translation systems are expected to cope with various types of
constraints in many practical scenarios. While neural machine translation (NMT)
has achieved strong performance in unconstrained cases, it is non-trivial to
impose pre-specified constraints into the translation process of NMT models.
Although many approaches have been proposed to address this issue, most
existing methods can not satisfy the following three desiderata at the same
time: (1) high translation quality, (2) high match accuracy, and (3) low
latency. In this work, we propose a template-based method that can yield
results with high translation quality and match accuracy and the inference
speed of our method is comparable with unconstrained NMT models. Our basic idea
is to rearrange the generation of constrained and unconstrained tokens through
a template. Our method does not require any changes in the model architecture
and the decoding algorithm. Experimental results show that the proposed
template-based approach can outperform several representative baselines in both
lexically and structurally constrained translation tasks.

本文提出了一种基于模板的方法，通过重新排列约束和非约束 token 的生成，实现高质量和高匹配精度的翻译结果，同时推断速度与非约束 NMT 模型相当，不需要对模型架构和解码算法作任何更改。实验结果表明，所提出的模板方法在词汇和结构约束翻译任务中优于几个代表性的基线。

基于模板的约束神经机器翻译方法

A Template-based Method for Constrained Neural Machine Translation

There is a recent interest in investigating few-shot NER, where the
low-resource target domain has different label sets compared with a
resource-rich source domain. Existing methods use a similarity-based metric.
However, they cannot make full use of knowledge transfer in NER model
parameters. To address the issue, we propose a template-based method for NER,
treating NER as a language model ranking problem in a sequence-to-sequence
framework, where original sentences and statement templates filled by candidate
named entity span are regarded as the source sequence and the target sequence,
respectively. For inference, the model is required to classify each candidate
span based on the corresponding template scores. Our experiments demonstrate
that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource
task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and
11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS
(low-resource task), respectively.

该论文提出了一种基于模板的方法，将 NER 视为一种序列到序列的语言模型排序问题，在推理过程中，模型需要根据相应的模板分数对每个候选实体进行分类，实验结果表明，该方法在 CoNLL03 数据集上取得了 92.55% 的 F1 得分，在 MIT Movie、MIT Restaurant 和 ATIS 数据集上也分别比 BERT 微调方法显着提高了 10.88%、15.34% 和 11.73% 的 F1 得分。