Recent advancements in morpheme segmentation primarily emphasize word-level
segmentation, often neglecting the contextual relevance within the sentence. In
this study, we redefine the morpheme segmentation task as a
sequence-to-sequence problem, treating the entire sentence as input rather than
isolating individual words. Our findings reveal that the multilingual model
consistently exhibits superior performance compared to monolingual
counterparts. While our model did not surpass the performance of the current
state-of-the-art, it demonstrated comparable efficacy with high-resource
languages while revealing limitations in low-resource language scenarios.

通过将整个句子作为输入，我们将形态素分割任务重新定义为序列到序列问题，结果显示多语种模型在性能上优于单语种模型，尽管没有超过最先进性能，但与高资源语言相比展现出了可比较的效果，同时揭示了在低资源语言情境下的限制。

利用背景信息进行句子级词素切割

Using Contextual Information for Sentence-level Morpheme 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.

该研究介绍了 SIGMORPHON 2022 共享任务关于形态素分割，试图将单词分解为一系列形态素，并覆盖了大部分类型的形态学。结果表明，该任务的提交结果对于单词级别和句子级别的形态素分割与现有的 3 种子单词分割方法相比有显著提高，为错误分析和未来的研究提供了有用的数据。