We demonstrate that a dependency parser can be built using a credit
assignment compiler which removes the burden of worrying about low-level
machine learning details from the parser implementation. The result is a simple
parser which robustly applies to many languages that provides similar
statistical and computational performance with best-to-date transition-based
parsing approaches, while avoiding various downsides including randomization,
extra feature requirements, and custom learning algorithms.

本文提出使用 credit assignment compiler 构建依存句法分析器，消除底层机器学习细节带来的负担。该方法得到了一个简单的解析器，可以应用于多种语言，并具有与最先进的基于转换的解析方法相似的统计和计算性能，同时避免了各种不利因素，包括随机化、额外的特征要求和定制化学习算法。

学习搜索依赖关系

Learning to Search for Dependencies

Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, implementing them has been burdensomely awkward. In this paper, we
show the search space can be defined by an arbitrary imperative program,
turning learning to search into a credit assignment compiler. Altogether with
the algorithmic improvements for the compiler, we radically reduce the
complexity of programming and the running time. We demonstrate the feasibility
of our approach on multiple joint prediction tasks. In all cases, we obtain
accuracies as high as alternative approaches, at drastically reduced execution
and programming time.

本文介绍了一种新的学习搜索方法，通过将复杂的决策问题转化为搜索空间的一系列决策，从而实现多个相互依赖输出变量的联合预测任务，并将其定义为任意命令程序的搜索空间，通过算法改进和编译器将编程和运行时间的复杂度大大降低，其效果在多个预测任务中获得了与替代方法相当的准确性。