解决规范分割的低资源挑战
Tackling the Low-resource Challenge for Canonical Segmentation
摘要：
典型的形态学切分包括将词划分为标准化的词素。在这里，我们感兴趣的任务时，训练数据是有限的方法。我们比较模型的性能，在模拟低资源设置的高资源语言德语，英语和印度尼西亚语的新数据集上的实验，真正的低资源语言Popoluca和Tepehua。我们探索了两个新的任务模型，借鉴了形态生成的密切相关领域：LSTM指针生成器和序列到序列模型，具有通过模仿学习训练的硬单调注意力。我们发现，在低资源环境中，新方法在所有语言上的准确率高达11.4%。然而，尽管所有语言在模拟低资源场景中的准确率都超过了50%，但对于真正的低资源语言Popoluca和Tepehua，我们的最佳模型分别仅获得了37.4%和28.4%的准确率。因此，我们得出结论，规范分割仍然是一个具有挑战性的任务，低资源的语言。
英文摘要：
Canonical morphological segmentation consists of dividing words into their standardized morphemes. Here, we are interested in approaches for the task when training data is limited. We compare model performance in a simulated low-resource setting for the high-resource languages German, English, and Indonesian to experiments on new datasets for the truly low-resource languages Popoluca and Tepehua. We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning. We find that, in the low-resource setting, the novel approaches outperform existing ones on all languages by up to 11.4% accuracy. However, while accuracy in emulated low-resource scenarios is over 50% for all languages, for the truly low-resource languages Popoluca and Tepehua, our best model only obtains 37.4% and 28.4% accuracy, respectively. Thus, we conclude that canonical segmentation is still a challenging task for low-resource languages.
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。
