The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections. Subtask 1, word-level morpheme segmentation, covered 5 million words in 9 languages (Czech, English, Spanish, Hungarian, French, Italian, Russian, Latin, Mongolian) and received 13 system submissions from 7 teams and the best system averaged 97.29% F1 score across all languages, ranging English (93.84%) to Latin (99.38%). Subtask 2, sentence-level morpheme segmentation, covered 18,735 sentences in 3 languages (Czech, English, Mongolian), received 10 system submissions from 3 teams, and the best systems outperformed all three state-of-the-art subword tokenization methods (BPE, ULM, Morfessor2) by 30.71% absolute. To facilitate error analysis and support any type of future studies, we released all system predictions, the evaluation script, and all gold standard datasets.

PDF Abstract NAACL (SIGMORPHON) 2022 PDF NAACL (SIGMORPHON) 2022 Abstract

Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Morpheme Segmentaiton UniMorph 4.0 Morfessor2 macro avg (subtask 1) 25.57 # 8
f1 macro avg (subtask 2) 50.65 # 11
lev dist (subtask 2) 12.08 # 7
Morpheme Segmentaiton UniMorph 4.0 ULM macro avg (subtask 1) 20.61 # 9
f1 macro avg (subtask 2) 45.99 # 12
lev dist (subtask 2) 14.28 # 8
Morpheme Segmentaiton UniMorph 4.0 WordPiece macro avg (subtask 1) 15.89 # 10
f1 macro avg (subtask 2) 40.59 # 13
lev dist (subtask 2) 17.54 # 9

Methods


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