With the increasing interest in low-resource
languages, unsupervised morphological segmentation has become an active area of research, where approaches based on Adaptor Grammars achieve state-of-the-art results.
We demonstrate the power of harnessing linguistic knowledge as priors within Adaptor
Grammars in a minimally-supervised learning
fashion. We introduce two types of priors:
1) grammar definition, where we design
language-specific grammars; and 2) linguistprovided affixes, collected by an expert in the
language and seeded into the grammars. We
use Japanese and Georgian as respective case
studies for the two types of priors and introduce new datasets for these languages, with
gold morphological segmentation for evaluation. We show that the use of priors results in
error reductions of 8.9 % and 34.2 %, respectively, over the equivalent state-of-the-art unsupervised system
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Lightweight morpheme labeling in context: Using structured linguistic representations to support linguistic analysis for the language documentation context
Linguistic analysis is a core task in the process of documenting, analyzing, and describing endangered and less-studied languages. In addition to providing insight into the properties of the language being studied, having tools to automatically label words in a language for grammatical category and morphological features can support a range of applications useful for language pedagogy and revitalization. At the same time, most modern NLP methods for these tasks require both large amounts of data in the language and compute costs well beyond the capacity of most research groups and language communities. In this paper, we present a gloss-to-gloss (g2g) model for linguistic analysis (specifically, morphological analysis and part-of-speech tagging) that is lightweight in terms of both data requirements and computational expense. The model is designed for the interlinear glossed text (IGT) format, in which we expect the source text of a sentence in a low-resource language, a translation of that sentence into a language of wider communication, and a detailed glossing of the morphological properties of each word in the sentence. We first produce silver standard parallel glossed data by automatically labeling the high-resource translation. The model then learns to transform source language morphological labels into output labels for the target language, mediated by a structured linguistic representation layer. We test the model on both low-resource and high-resource languages, and find that our simple CNN-based model achieves comparable performance to a state-of-the-art transformer-based model, at a fraction of the computational cost.
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- Award ID(s):
- 2149404
- PAR ID:
- 10539619
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Page Range / eLocation ID:
- 78 to 92
- Format(s):
- Medium: X
- Location:
- Toronto, Canada
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2023.sigmorphon-1.9
