Contemporary multilingual dependency parsers can parse a diverse set of languages, but for Morphologically Rich Languages (MRLs), performance is attested to be lower than other languages. The key challenge is that, due to high morphological complexity and ambiguity of the space-delimited input tokens, the linguistic units that act as nodes in the tree are not known in advance. Pre-neural dependency parsers for MRLs subscribed to the joint morpho-syntactic hypothesis, stating that morphological segmentation and syntactic parsing should be solved jointly, rather than as a pipeline where segmentation precedes parsing. However, neural state-of-the-art parsers to date use a strict pipeline. In this paper we introduce a joint neural architecture where a lattice-based representation preserving all morphological ambiguity of the input is provided to an arc-factored model, which then solves the morphological segmentation and syntactic parsing tasks at once. Our experiments on Hebrew, a rich and highly ambiguous MRL, demonstrate state-of-the-art performance on parsing, tagging and segmentation of the Hebrew section of UD, using a single model. This proposed architecture is LLM-based and language agnostic, providing a solid foundation for MRLs to obtain further performance improvements and bridge the gap with other languages.

翻译：当代多语言依存句法分析器能够处理多种语言，但针对形态丰富的语言（MRLs），其性能被证实低于其他语言。关键挑战在于：由于空格分隔的输入词元具有高度形态复杂性和歧义性，作为树结构中节点的语言单元无法预先确定。前神经时代的MRL依存句法分析器遵循形态-句法联合假设，主张形态分词与句法分析应联合求解，而并非采用分词先于句法的流水线方法。然而，当前最先进的神经句法分析器仍采用严格的流水线架构。本文提出一种联合神经架构：将保留输入所有形态歧义的基于格子的表示提供给弧分解模型，从而一次性解决形态分词与句法分析任务。我们在形态丰富且高度歧义的希伯来语上的实验表明，使用单一模型即可在UD希伯来语语料库的分词、词性标注及句法分析任务上取得最先进性能。该架构基于大语言模型且语言无关，为MRLs获得进一步性能提升并缩小与其他语言的差距奠定了坚实基础。