This paper outlines a theoretical framework using which different automatic
metrics can be designed for evaluation of Machine Translation systems. It
introduces the concept of {\em cognitive ease} which depends on {\em adequacy}
and {\em lack of fluency}. Thus, cognitive ease becomes the main parameter to
be measured rather than comprehensibility. The framework allows the components
of cognitive ease to be broken up and computed based on different linguistic
levels etc. Independence of dimensions and linearly combining them provides for
a highly modular approach.
The paper places the existing automatic methods in an overall framework, to
understand them better and to improve upon them in future. It can also be used
to evaluate the newer types of MT systems, such as speech to speech translation
and discourse translation.

本篇论文提出了一个理论框架，可以设计不同的自动度量标准来评估机器翻译系统。它引入了 “认知轻松度” 概念，依赖于 “适当性” 和 “流畅度”。这样，认知轻松度成为要测量的主要参数，而不是可理解性。该框架允许对认知轻松度的各个组成部分进行分解和基于不同的语言层次进行计算。此外，维度的独立性和线性组合方法为高度模块化的方法提供了可能性。该论文将现有的自动方法置于一个整体框架中，以更好地理解它们并在未来改进它们。它也可以用来评估新类型的 MT 系统，例如语音到语音翻译和语篇翻译。

评估机器翻译系统：一个理论框架

Evaluating MT Systems: A Theoretical Framework

Transfer and multi-task learning have traditionally focused on either a
single source-target pair or very few, similar tasks. Ideally, the linguistic
levels of morphology, syntax and semantics would benefit each other by being
trained in a single model. We introduce a joint many-task model together with a
strategy for successively growing its depth to solve increasingly complex
tasks. Higher layers include shortcut connections to lower-level task
predictions to reflect linguistic hierarchies. We use a simple regularization
term to allow for optimizing all model weights to improve one task's loss
without exhibiting catastrophic interference of the other tasks. Our single
end-to-end model obtains state-of-the-art or competitive results on five
different tasks from tagging, parsing, relatedness, and entailment tasks.

本研究提出一种多任务学习模型，可以同时训练多种语言学层次的任务，并通过一种逐步增加深度的策略来解决越来越复杂的任务。该模型使用一个简单的正则化项来实现在提高某一任务的损失函数时，不影响其他任务的学习效果。实验结果表明，这个端到端的模型在标签、句法分析、相关性与蕴含等五种任务上取得了最先进或有竞争力的结果。