TAMS: Translation-Assisted Morphological Segmentation

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.

GlossLM: Multilingual Pretraining for Low-Resource Interlinear Glossing

A key aspect of language documentation is the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. Prior work has explored methods to automatically generate IGT in order to reduce the time cost of language analysis. However, many languages (particularly those requiring preservation) lack sufficient IGT data to train effective models, and crosslingual transfer has been proposed as a method to overcome this limitation. We compile the largest existing corpus of IGT data from a variety of sources, covering over 450k examples across 1.8k languages, to enable research on crosslingual transfer and IGT generation. Then, we pretrain a large multilingual model on a portion of this corpus, and further finetune it to specific languages. Our model is competitive with state-of-the-art methods for segmented data and large monolingual datasets. Meanwhile, our model outperforms SOTA models on unsegmented text and small corpora by up to 6.6% morpheme accuracy, demonstrating the effectiveness of crosslingual transfer for low-resource languages.