Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes. This is a core task in language documentation, and NLP systems have the potential to dramatically speed up this process. But in typical language documentation settings, training data for canonical morpheme segmentation is scarce, making it difficult to train high quality models. However, translation data is often much more abundant, and, in this work, we present a method that attempts to leverage this data in the canonical segmentation task. We propose a character-level sequence-to-sequence model that incorporates representations of translations obtained from pretrained high-resource monolingual language models as an additional signal. Our model outperforms the baseline in a super-low resource setting but yields mixed results on training splits with more data. While further work is needed to make translations useful in higher-resource settings, our model shows promise in severely resource-constrained settings.

标准词素分析是将单词分析为其组成形态素的标准（即底层）形式的过程。本文提出了一种利用先前经过训练的高资源单语言模型获得的翻译的字符级序列到序列模型来改善标准分词任务的方法，取得了在无资源情况下超越基准的效果，但在训练数据更多的情况下结果不稳定。尽管在高资源环境中使用翻译仍需进一步研究，但我们的模型在资源极度有限的情况下展现出希望。

TAMS：基于翻译的形态分割