CoNLL-SIGMORPHON 2017共享任务：52种语言的通用形态重变
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages
摘要：
CoNLL-SIGMORPHON 2017共享的监督形态生成任务要求系统在52种类型学上不同的语言中进行训练和测试。在子任务1中，提交的系统被要求预测给定引理的特定变形形式。在子任务2中，系统被给予一个引理及其一些特定的屈折形式，并被要求通过预测所有剩余的屈折形式来完成屈折范式。这两个子任务包括高、中和低资源条件。子任务1收到24个系统提交，而子任务2收到3个系统提交。继神经序列到序列模型在SIGMORPHON 2016共享任务中取得成功之后，除了一个提交的文件外，所有提交的文件都包含了神经组件。结果表明，只要模型具有适当的归纳偏差，或者利用额外的未标记数据或合成数据，就可以在较小的训练数据集上获得较高的性能。然而，不同的偏置和数据增强导致正确预测不相交的变形形式集，这表明未来还有改进的空间。
英文摘要：
The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific inflected form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in disjoint sets of inflected forms being predicted correctly, suggesting that there is room for future improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
