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It has been exactly a decade since the first establishment of SPMRL, aresearch initiative unifying multiple research efforts to address the peculiarchallenges of Statistical Parsing for Morphologically-Rich Languages(MRLs).Here we reflect on parsing MRLs in that decade, highlight the solutionsand lessons learned for the architectural, modeling and lexical challenges inthe pre-neural era, and argue that similar challenges re-emerge in neuralarchitectures for MRLs. We then aim to offer a climax, suggesting thatincorporating symbolic ideas proposed in SPMRL terms into nowadays neuralarchitectures has the potential to push NLP for MRLs to a new level. We sketchstrategies for designing Neural Models for MRLs (NMRL), and showcasepreliminary support for these strategies via investigating the task ofmulti-tagging in Hebrew, a morphologically-rich, high-fusion, language

From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?