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SIGMORPHON 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation

1 Jul 2022NAACL (SIGMORPHON) 2022 7archive 2025-07-28

Leander Girrbach

We propose a sequence labelling approach to word-level morpheme segmentation. Segmentation labels are edit operations derived from a modified minimum edit distance alignment. We show that sequence labelling performs well for “shallow segmentation” and “canonical segmentation”, achieving 96.06 f1 score (macroaveraged over all languages in the shared task) and ranking 3rd among all participating teams. Therefore, we conclude that sequence labelling is a promising approach to morpheme segmentation.

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Tasks

AllMorpheme SegmentaitonSegmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Morpheme Segmentaiton UniMorph 4.0 BiLSTM for seq labelling (Tü_Seg-1) macro avg (subtask 1) 96.06 #5 of 19 Archive leaderboard report
Morpheme Segmentaiton UniMorph 4.0 BiLSTM for seq labelling (Tü_Seg-2) f1 macro avg (subtask 2) 82.07 #16 of 19 Archive leaderboard report
Morpheme Segmentaiton UniMorph 4.0 BiLSTM for seq labelling (Tü_Seg-2) lev dist (subtask 2) 4.71 #16 of 19 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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