LexC-Gen: Generating Data for Extremely Low-Resource Languages with
  Large Language Models and Bilingual Lexicons
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.
65d7f0e5baa72790a127d85b
Zheng-Xin Yong
claimed_verified
2024-02-23T13:05:36.686Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/61424bf4f0d914a5f606a823/0td8lR4elBaVvJUD9Pojh.png
Yong Zheng-Xin
user
yongzx
65d7f0e5baa72790a127d85c
Cristina Menghini
admin_assigned
2024-02-23T13:04:53.289Z
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Cristina Menghini
user
crimenghini
65d7f0e5baa72790a127d85d
Stephen H. Bach
admin_assigned
2024-02-23T13:04:37.687Z
608849cadf398c3b285ce95b
https://cdn-avatars.huggingface.co/v1/production/uploads/608849cadf398c3b285ce95b/roABeBreO9pZRPxzVoOPM.jpeg
Stephen Bach
user
SteveBach
LexC-Gen: Generating Data for Extremely Low-Resource Languages with
  Large Language Models and Bilingual Lexicons
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.
A method for generating task-specific data in low-resource languages using bilingual lexicons and outperforming existing translation methods in sentiment analysis and topic classification.
bilingual lexicons word-to-word translations lexicon-conditioned data generation sentiment analysis topic classification LLMs