Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages

• URL: http://arxiv.org/abs/2110.08415v1
• Date: Sat, 16 Oct 2021 00:08:28 GMT
• Title: Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages
• Authors: C.M. Downey, Shannon Drizin, Levon Haroutunian, Shivin Thukral
• Abstract summary: Unsupervised sequence-segmentation performance can be transferred to extremely low-resource languages by pre-training a Masked Segmental Language Model multilingually. We show that this transfer can be achieved by training over a collection of low-resource languages that are typologically similar (but phylogenetically unrelated) to the target language.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We show that unsupervised sequence-segmentation performance can be transferred to extremely low-resource languages by pre-training a Masked Segmental Language Model (Downey et al., 2021) multilingually. Further, we show that this transfer can be achieved by training over a collection of low-resource languages that are typologically similar (but phylogenetically unrelated) to the target language. In our experiments, we transfer from a collection of 10 Indigenous American languages (AmericasNLP, Mager et al., 2021) to K'iche', a Mayan language. We compare our model to a monolingual baseline, and show that the multilingual pre-trained approach yields much more consistent segmentation quality across target dataset sizes, including a zero-shot performance of 20.6 F1, and exceeds the monolingual performance in 9/10 experimental settings. These results have promising implications for low-resource NLP pipelines involving human-like linguistic units, such as the sparse transcription framework proposed by Bird (2020).

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