  Semantic role labeling (SRL) enriches many downstream applications, e.g.,
machine translation, question answering, summarization, and stance/belief
detection. However, building multilingual SRL models is challenging due to the
scarcity of semantically annotated corpora for multiple languages. Moreover,
state-of-the-art SRL projection (XSRL) based on large language models (LLMs)
yields output that is riddled with spurious role labels. Remediation of such
hallucinations is not straightforward due to the lack of explainability of
LLMs. We show that hallucinated role labels are related to naturally occurring
divergence types that interfere with initial alignments. We implement
Divergence-Aware Hallucination-Remediated SRL projection (DAHRS), leveraging
linguistically-informed alignment remediation followed by greedy First-Come
First-Assign (FCFA) SRL projection. DAHRS improves the accuracy of SRL
projection without additional transformer-based machinery, beating XSRL in both
human and automatic comparisons, and advancing beyond headwords to accommodate
phrase-level SRL projection (e.g., EN-FR, EN-ES). Using CoNLL-2009 as our
ground truth, we achieve a higher word-level F1 over XSRL: 87.6% vs. 77.3%
(EN-FR) and 89.0% vs. 82.7% (EN-ES). Human phrase-level assessments yield 89.1%
(EN-FR) and 91.0% (EN-ES). We also define a divergence metric to adapt our
approach to other language pairs (e.g., English-Tagalog).
