Sema System for the DMR 2026 Shared Task: Multistage UMR Parsing with Qwen3-4B

Abstract

We present the Sema system for the DMR 2026 shared task on parsing from natural language to . Our approach relies on parameter-efficient fine-tuning of Qwen3-4B with a multistage training procedure. We first train on a capped subset of the noisy training data, then continue training on the clean split, and finally fine-tune a dedicated stage for word-to-node alignment prediction. The system generates sentence-level graphs, selected document-level information, and alignments in separate steps, followed by rule-based post-processing to satisfy the official evaluation format. Results show that the approach is viable across several languages and exhibits promising transfer to Italian despite the absence of Italian data for fine-tuning , while very low-resource languages remain challenging.

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