无需对齐语料，用词典实现跨语言主题模型迁移
TMT: A Simple Way to Translate Topic Models Using Dictionaries
- 基于词典构建主题模型跨语言映射，不依赖嵌入或对齐数据
- 在无目标语言大规模语料时仍保持主题语义一致性
- 适合低资源语言或缺乏目标语言知识的研究者使用
多语言环境下训练主题模型极具挑战，需复杂算法、对齐语料和人工评估。当开发者不熟悉目标语言或仅有小规模/不可用的多语语料时，这一挑战更加突出。为此，我们提出主题模型翻译（TMT），一种新颖、鲁棒且透明的方法，可将主题模型（如基于LDA的模型）从一种语言迁移到另一种语言，无需元数据、嵌入或对齐语料。TMT使主题模型在不同语言间复用成为可能，特别适用于目标语言缺乏大规模语料或人工翻译不可行的场景。我们通过定量与定性方法全面评估TMT，结果表明其生成的主题翻译在语义上连贯且一致。
原文摘要 · Abstract (English)
The training of topic models for a multilingual environment is a challenging task, requiring the use of sophisticated algorithms, topic-aligned corpora, and manual evaluation. These difficulties are further exacerbated when the developer lacks knowledge of the target language or is working in an environment with limited data, where only small or unusable multilingual corpora are available. Considering these challenges, we introduce Topic Model Translation (TMT), a novel, robust and transparent technique designed to transfer topic models (e.g., Latent Dirichlet Allocation (LDA) based topic models) from one language to another, without the need for metadata, embeddings, or aligned corpora. TMT enables the reuse of topic models across languages, making it especially suitable for scenarios where large corpora in the target language are unavailable or manual translation is infeasible. Furthermore, we evaluate TMT extensively using both quantitative and qualitative methods, demonstrating that it produces semantically coherent and consistent topic translations.
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