1 citations · 2 across the 7 of their papers we have counts for
7 papers
Learning Beyond Limits: Multitask Learning and Synthetic Data for Low-Resource Canonical Morpheme Segmentation
Changbing Yang, Garrett Nicolai
We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framewor…
Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossing
Changbing Yang, Garrett Nicolai, Miikka Silfverberg
In this paper, we address the data scarcity problem in automatic data-driven glossing for low-resource languages by coordinating multiple sources of linguistic expertise. We supple…
Embedded Translations for Low-resource Automated Glossing
Changbing Yang, Garrett Nicolai, Miikka Silfverberg
We investigate automatic interlinear glossing in low-resource settings. We augment a hard-attentional neural model with embedded translation information extracted from interlinear…
An Investigation of Noise in Morphological Inflection
Adam Wiemerslage, Changbing Yang, Garrett Nicolai +2
With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We ai…
Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages
Clarissa Forbes, Farhan Samir, Bruce Harold Oliver +4
Recent progress in NLP is driven by pretrained models leveraging massive datasets and has predominantly benefited the world's political and economic superpowers. Technologically un…
Morphological Processing of Low-Resource Languages: Where We Are and What's Next
Adam Wiemerslage, Miikka Silfverberg, Changbing Yang +4
Automatic morphological processing can aid downstream natural language processing applications, especially for low-resource languages, and assist language documentation efforts for…
CLiMP: A Benchmark for Chinese Language Model Evaluation
Beilei Xiang, Changbing Yang, Yu Li +2
Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models. Here, we introduce the corpus of Chinese linguistic minim…