LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages (2405.08997v2)

Abstract: 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.

• The paper introduces an LLM-assisted rule-based MT approach that achieves 98% accuracy on simple sentence translations for endangered, no-resource languages.
• It leverages a rule-based sentence builder combined with few-shot LLM prompting to translate between Owens Valley Paiute and English.
• The method offers practical benefits for language education, synthetic data generation, and revitalization of critically endangered languages.

LLM-Assisted Rule-Based Machine Translation for No-Resource Languages

Introduction

So, you're knee-deep in the world of data science, and you understand the power and potential of LLMs and NLP (Natural Language Processing). But, have you ever stopped to think about how these impressive models perform when there’s almost no data to train them on? This is the challenge faced when dealing with "no-resource" languages, languages without any publicly available bilingual or monolingual corpora.

Let's explore a novel approach discussed in recent research: LLM-Assisted Rule-Based Machine Translation (LLM-RBMT). This approach aims to leverage the strengths of LLMs to assist in translating and revitalizing critically endangered languages— like Owens Valley Paiute (OVP).

Why Focus on Owens Valley Paiute?

Owens Valley Paiute (OVP) is a critically endangered Indigenous American language with virtually no publicly available data. The goal here is not to create a perfect universal translator but to help in language teaching and revitalization for a language learner trying to build simple sentences.

How Does LLM-RBMT Work?

This paradigm combines the structure of rule-based machine translation with the flexibility and contextual understanding of LLMs. Here's a breakdown of how it operates:

The Sentence Builder

The researchers developed an OVP sentence builder that allows users to construct valid sentences by selecting parts of speech like subjects, verbs, and objects from predefined lists. This rule-based approach ensures that the sentences are grammatically correct according to OVP rules.

Translating OVP to English

For translating OVP to English, the process involves converting the structured simple sentences from the sentence builder into natural English sentences using an LLM like GPT-3.5-turbo. Remarkably, this approach yields high accuracy, with 98 out of 100 randomly generated sentences translating correctly.

Here's what happens step-by-step:

1. Sentence Creation: The user picks words and constructs a simple sentence.
2. Encoding to Structured Data: This structured data captures the grammatical structure without needing the meanings embedded in complex sentences.
3. LLM Translation: Few-shot learning prompts the LLM to convert these structured sentences into natural English.

Translating English to OVP

The translation from English to OVP involves breaking down an input English sentence into a set of simpler structured sentences by the LLM. The process is rather intricate:

1. Sentence Segmentation: The LLM breaks down the English sentence into simpler subject-verb or subject-verb-object sentences.
2. OVP Sentence Creation: Using the sentence builder, these simpler sentences are translated into OVP.
3. Round-Trip Translation Verification: The OVP sentences are then translated back into English to verify the accuracy.

Evaluation Metrics

The translations were evaluated using semantic similarity scores, which compare the meanings of sentences without focusing on their grammatical structure. Seven different embeddings models were tested, with the all-MiniLM-L6-v2 model performing best for this task.

Results and Implications

Both GPT-3.5-turbo and GPT-4 yielded promising results. Here are some notable observations:

• High accuracy in translating simple sentences, especially subject-verb structures.
• The system is significantly limited by the available vocabulary but shows potential with partial translations.
• Future work might expand the vocabulary and improve prompt engineering to capture more contextual meaning.

Practical Implications

This approach opens up exciting possibilities:

1. Educational Tools: Language learners can use this tool to construct and understand simple sentences in endangered languages.
2. Data Augmentation: The system can help generate synthetic data for training more complex translation models.
3. Language Revitalization: Communities can leverage technology to keep their language alive and teach new generations.

Theoretical Implications and Future Directions

From a theoretical standpoint, this research shows that LLMs can be effective even without extensive native language datasets. This holds promise for the future development of translation systems for other no-resource languages.

Future research might explore:

• Expanding Vocabulary: Integrate more words to handle a broader range of sentences.
• Semantic Parsing: Improve how sentences are broken down and interpreted by the system.
• Broader Applications: Test the system with other critically endangered no-resource languages.

This LLM-RBMT approach is a step towards harnessing the advanced capabilities of LLMs to aid in the revitalization and education of endangered languages, ensuring they are not lost to time.