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.

翻译：摘要：规范形态切分是指将词语分析为其构成语素的标准（即底层）形式的过程。这是语言记录中的核心任务，自然语言处理系统有望大幅加快这一进程。然而，在典型的语言记录场景中，规范语素切分的训练数据极为稀缺，导致难以训练出高质量的模型。但翻译数据通常更为丰富，因此，本文提出一种方法，尝试在规范切分任务中利用这些数据。我们设计了一个字符级序列到序列模型，该模型将来自预训练高资源单语语言模型的翻译表示作为额外信号融入其中。在超低资源场景下，我们的模型性能优于基线，但在包含更多数据的训练子集上结果表现不一。尽管仍需进一步研究才能使翻译在更高资源场景中发挥作用，但我们的模型在资源极度受限的情况下已展现出潜力。