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Main Authors: Coleman, Jared, Krishnamachari, Bhaskar, Iskarous, Khalil, Rosales, Ruben
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2405.08997
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author Coleman, Jared
Krishnamachari, Bhaskar
Iskarous, Khalil
Rosales, Ruben
author_facet Coleman, Jared
Krishnamachari, Bhaskar
Iskarous, Khalil
Rosales, Ruben
contents We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation). Using the LLM-RBMT paradigm, we design the first language education/revitalization-oriented machine translator for Owens Valley Paiute (OVP), a critically endangered Indigenous American language for which there is virtually no publicly available data. We present a detailed evaluation of the translator's components: a rule-based sentence builder, an OVP to English translator, and an English to OVP translator. We also discuss the potential of the paradigm, its limitations, and the many avenues for future research that it opens up.
format Preprint
id arxiv_https___arxiv_org_abs_2405_08997
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages
Coleman, Jared
Krishnamachari, Bhaskar
Iskarous, Khalil
Rosales, Ruben
Computation and Language
We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation). Using the LLM-RBMT paradigm, we design the first language education/revitalization-oriented machine translator for Owens Valley Paiute (OVP), a critically endangered Indigenous American language for which there is virtually no publicly available data. We present a detailed evaluation of the translator's components: a rule-based sentence builder, an OVP to English translator, and an English to OVP translator. We also discuss the potential of the paradigm, its limitations, and the many avenues for future research that it opens up.
title LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages
topic Computation and Language
url https://arxiv.org/abs/2405.08997