The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
This work addresses the problem of accurate morpheme segmentation for computational linguists and NLP researchers, providing benchmarks and resources, but it is incremental as it builds on existing shared tasks and methods.
The SIGMORPHON 2022 shared task tackled morpheme segmentation at word and sentence levels across multiple languages, with the best system achieving an average F1 score of 97.29% in word-level segmentation and outperforming state-of-the-art subword tokenization methods by 30.71% absolute in sentence-level segmentation.
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.