We propose a framework using Energy Based Models for multiple structured prediction tasks in Sanskrit. Ours is an arc-factored model, similar to the graph-based parsing approaches, and we consider the tasks of word segmentation, morphological parsing, dependency parsing, syntactic linearization, and prosodification, a prosody-level task we introduce in this work. Ours is a search-based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes. Typically, the state of the art models for morphosyntactic tasks in morphologically rich languages still rely on hand-crafted features for their performance. But here, we automate the learning of the feature function. The feature function so learned, along with the search space we construct, encode relevant linguistic information for the tasks we consider. This enables us to substantially reduce the training data requirements to as low as 10 % as compared to the data requirements for the neural state of the art models. Our experiments in Czech and Sanskrit show the language-agnostic nature of the framework, where we train highly competitive models for both the languages. Moreover, our framework enables us to incorporate languagespecific constraints to prune the search space and to filter the candidates during inference. We obtain significant improvements in morphosyntactic tasks for Sanskrit by incorporating language-specific constraints into the model. In all the tasks we discuss for Sanskrit, we either achieve state of the art results or ours is the only data-driven solution for those tasks.

中文翻译：

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用于梵文结构化预测任务的基于图的框架

我们提出了一个使用基于能量的模型的框架，用于梵文中的多个结构化预测任务。我们的模型是一个弧因子模型，类似于基于图的解析方法，我们考虑了分词、形态解析、依存解析、句法线性化和韵律化的任务，这是我们在这项工作中引入的韵律级别的任务。我们是一个基于搜索的结构化预测框架，它需要一个图作为输入，其中相关的语言信息在节点中编码，然后使用边来指示这些节点之间的关联。通常，在形态丰富的语言中用于形态句法任务的最先进模型仍然依赖于手工制作的特征来实现它们的性能。但在这里，我们自动化了特征函数的学习。这样学到的特征函数，连同我们构建的搜索空间，为我们考虑的任务编码相关的语言信息。与最先进的神经模型的数据需求相比，这使我们能够将训练数据需求大幅降低至低至 10%。我们在捷克语和梵语中的实验显示了该框架与语言无关的性质，我们为这两种语言训练了极具竞争力的模型。此外，我们的框架使我们能够结合特定于语言的约束来修剪搜索空间并在推理过程中过滤候选者。通过将特定于语言的约束合并到模型中，我们在梵语的形态句法任务方面获得了显着改进。在我们为梵文讨论的所有任务中，我们要么实现最先进的结果，要么我们是这些任务的唯一数据驱动解决方案。