Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages.

翻译：在自然语言处理应用程序中,产生文本的系统是普遍存在的。然而,对这些系统的评估仍然是个挑战,特别是在多语种环境中。在本文中,我们提议L'AMBRE -- -- 使用其依赖性分析法和该语言的形态分析法规则来评价文本的形态化完善性。我们提出了一个方法,可以直接从依赖性树库中自动提取关于形态化法的各种规则。为了解决文本生成系统产生的噪音,我们提议了一个简单的方法来培训强大的分析员。我们通过对形态化语言转换系统进行对等时间化研究,展示了我们机器翻译任务衡量标准的有效性。