LexC-Gen：利用大型語言模型和雙語詞典為極低資源語言生成數據

摘要

在低資源語言中的數據稀缺問題可以通過使用雙語詞典，從高資源語言中標記的任務數據進行詞對詞翻譯來解決。然而，雙語詞典通常與任務數據的詞彙重疊有限，導致翻譯覆蓋率和詞典利用率不佳。我們提出了一種稱為詞典條件數據生成（LexC-Gen）的方法，可以大規模生成低資源語言的分類任務數據。具體來說，LexC-Gen首先使用雙語詞典中的高資源語言詞彙生成與詞典相容的任務數據，然後通過詞彙翻譯將其翻譯為低資源語言。在17種極低資源語言中，LexC-Gen生成的數據與專家翻譯的標金數據相競爭，並在情感分析和主題分類任務上分別平均提高了5.6和8.9個分數，優於現有基於詞典的詞彙翻譯方法。我們表明，以雙語詞典為條件是LexC-Gen的關鍵組成部分。LexC-Gen也很實用，僅需一個單GPU即可大規模生成數據。它與開放存取的LLMs配合良好，成本僅為基於GPT4的多語種數據生成的五分之一。

English

Data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons. However, bilingual lexicons often have limited lexical overlap with task data, which results in poor translation coverage and lexicon utilization. We propose lexicon-conditioned data generation (LexC-Gen), a method that generates low-resource-language classification task data at scale. Specifically, LexC-Gen first uses high-resource-language words from bilingual lexicons to generate lexicon-compatible task data, and then it translates them into low-resource languages with bilingual lexicons via word translation. Across 17 extremely low-resource languages, LexC-Gen generated data is competitive with expert-translated gold data, and yields on average 5.6 and 8.9 points improvement over existing lexicon-based word translation methods on sentiment analysis and topic classification tasks respectively. We show that conditioning on bilingual lexicons is the key component of LexC-Gen. LexC-Gen is also practical -- it only needs a single GPU to generate data at scale. It works well with open-access LLMs, and its cost is one-fifth of the cost of GPT4-based multilingual data generation.

LexC-Gen：利用大型語言模型和雙語詞典為極低資源語言生成數據

LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons

摘要

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