The paper discusses the challenges faced by multilingual dependency parsers in handling Morphologically Rich Languages (MRLs). It proposes a joint neural architecture that combines morphological segmentation and syntactic parsing tasks. The experiments conducted on Hebrew demonstrate significant performance improvements, showcasing the effectiveness of the proposed model.
The content highlights the importance of accurate segmentation for successful parsing in MRLs. It compares various approaches, including pre-neural models and neural architectures, emphasizing the benefits of a unified solution. The study provides insights into how linguistic units are processed in complex languages like Hebrew, shedding light on the intricacies involved in parsing such languages.
Furthermore, the paper explores different scenarios, such as using gold vs. predicted segmentations, and evaluates the impact of Multitask Learning (MTL) components on segmentation, tagging, and parsing accuracy. The results indicate that the proposed architecture outperforms existing models and offers a promising solution for handling MRLs efficiently.
Overall, the research contributes to advancing NLP techniques for challenging language structures and lays a foundation for further improvements in parsing methodologies for diverse languages.
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arxiv.org
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