title: LexC-Gen: Generating Data for Extremely Low-Resource Languages with
    Large Language Models and Bilingual Lexicons
summary: 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.
paper_authors._id: 65d7f0e5baa72790a127d85b
paper_authors.name: Zheng-Xin Yong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-23T13:05:36.686Z
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paper_authors.fullname: Yong Zheng-Xin
paper_authors.type: user
paper_authors.user: yongzx
paper_authors._id: 65d7f0e5baa72790a127d85c
paper_authors.name: Cristina Menghini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-23T13:04:53.289Z
paper_authors._id: 64809d8e8c6a3b8f11f929ce
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paper_authors.fullname: Cristina Menghini
paper_authors.type: user
paper_authors.user: crimenghini
paper_authors._id: 65d7f0e5baa72790a127d85d
paper_authors.name: Stephen H. Bach
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-23T13:04:37.687Z
paper_authors._id: 608849cadf398c3b285ce95b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/608849cadf398c3b285ce95b/roABeBreO9pZRPxzVoOPM.jpeg
paper_authors.fullname: Stephen Bach
paper_authors.type: user
paper_authors.user: SteveBach
paper_title: LexC-Gen: Generating Data for Extremely Low-Resource Languages with
    Large Language Models and Bilingual Lexicons
paper_summary: 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.
paper_ai_keywords: bilingual lexicons lexicon-conditioned data generation (LexC-Gen) lexicon-compatible task data word translation low-resource-language classification task data high-resource-language words sentiment analysis topic classification tasks expert-translated gold data open-access LLMs GPT4-based multilingual data generation