无需烦恼的 MRL 句法分析：以希伯来语为例
MRL Parsing Without Tears: The Case of Hebrew
AI总结：
针对形态丰富语言句法分析的误差传播与高延迟问题，论文提出面向希伯来语的独立专家分类器“翻转流水线”，在 POS 标注和依存分析上达到 SOTA。
摘要：
句法分析仍然是关系抽取和信息抽取的关键工具，尤其是在缺乏 LLM 的资源稀缺语言中。然而，在形态丰富语言（MRL）中，分析器需要识别每个 token 中的多个词汇单元，现有系统在延迟和设置复杂性方面表现不佳。一些系统使用流水线来逐层剥离：首先分词，然后进行形态标注，接着进行句法分析；然而，较早层级的错误随后会向前传播。另一些系统使用联合架构一次性评估所有排列；虽然这提高了准确率，但其速度之慢众所周知。与之相反，本文以希伯来语作为测试案例，提出一种新的“翻转流水线”：由专家分类器直接在整个 token 单元上作出决策，每个分类器专门负责一项特定任务。这些分类器彼此独立，只有在最后才综合它们的预测。这种极快的方法在希伯来语 POS 标注和依存句法分析上确立了新的 SOTA，同时在其他希伯来语 NLP 任务上达到接近 SOTA 的性能。由于我们的架构不依赖任何语言特定资源，它可以作为开发其他 MRL 类似分析器的模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
