Syntactic parsing remains a critical tool for relation extraction and information extraction, especially in resource-scarce languages where LLMs are lacking. Yet in morphologically rich languages (MRLs), where parsers need to identify multiple lexical units in each token, existing systems suffer in latency and setup complexity. Some use a pipeline to peel away the layers: first segmentation, then morphology tagging, and then syntax parsing; however, errors in earlier layers are then propagated forward. Others use a joint architecture to evaluate all permutations at once; while this improves accuracy, it is notoriously slow. In contrast, and taking Hebrew as a test case, we present a new "flipped pipeline": decisions are made directly on the whole-token units by expert classifiers, each one dedicated to one specific task. The classifiers are independent of one another, and only at the end do we synthesize their predictions. This blazingly fast approach sets a new SOTA in Hebrew POS tagging and dependency parsing, while also reaching near-SOTA performance on other Hebrew NLP tasks. Because our architecture does not rely on any language-specific resources, it can serve as a model to develop similar parsers for other MRLs.

翻译：句法解析仍然是关系抽取和信息抽取的关键工具，尤其是在大语言模型匮乏的资源稀缺语言中。然而，在形态丰富语言中，解析器需要识别每个词元中的多个词汇单元，现有系统在延迟和设置复杂性方面存在不足。有些系统采用流水线方式逐层剥离：先进行分词，然后进行形态标注，最后进行句法解析；但前一层中的错误会向后传播。另一些系统则采用联合架构一次性评估所有排列组合；虽然这提高了准确性，但速度极慢。相比之下，以希伯来语为测试案例，我们提出了一种新的“反向流水线”：由专门的分类器直接对整个词元单元做出决策，每个分类器负责一个特定任务。这些分类器相互独立，仅在最后阶段综合其预测结果。这种极快的方法在希伯来语词性标注和依存句法解析中设定了新的最优水平，同时在希伯来语的其他自然语言处理任务中也达到了接近最优水平。由于我们的架构不依赖任何语言特定资源，它可以作为为其他形态丰富语言开发类似解析器的模型。