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CWoMP: Morpheme Representation Learning for Interlinear Glossing
Morris Alper, Enora Rice, Bhargav Shandilya +2
Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. Recent automated IGT methods treat…
Massively Multilingual Joint Segmentation and Glossing
Michael Ginn, Lindia Tjuatja, Enora Rice +5
Automated interlinear gloss prediction with neural networks is a promising approach to accelerate language documentation efforts. However, while state-of-the-art models like GlossL…
Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
Enora Rice, Katharina von der Wense, Alexis Palmer
Computational morphology has the potential to support language documentation through tasks like morphological segmentation and the generation of Interlinear Glossed Text (IGT). How…
Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging
Enora Rice, Ali Marashian, Hannah Haynie +2
Cross-lingual transfer learning is an invaluable tool for overcoming data scarcity, yet selecting a suitable transfer language remains a challenge. The precise roles of linguistic…
From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation
Ali Marashian, Enora Rice, Luke Gessler +2
Many of the world's languages have insufficient data to train high-performing general neural machine translation (NMT) models, let alone domain-specific models, and often the only…
TAMS: Translation-Assisted Morphological Segmentation
Enora Rice, Ali Marashian, Luke Gessler +2
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 docu…