为跨语言迁移设计匹配语言类型的精准距离度量
Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+
- 按语言类型定制表示：地理用加权分布，谱系用双曲嵌入，类型学用隐变量模型
- 在零样本迁移任务中，相关类型距离提升显著，综合距离在多数任务上表现更优
- 适合需要跨语言知识迁移的研究者，尤其关注语言结构差异的场景
现有语言知识库如 URIEL+ 为跨语言迁移提供了宝贵的地理、谱系和类型学距离，但存在两大局限：其一，统一向量表示难以适应语言数据的多样化结构；其二，缺乏将这些信号整合为单一综合评分的合理方法。本文提出一种类型匹配的语言距离框架，为每种距离类型设计结构感知表示：地理采用说话人加权分布，谱系使用双曲嵌入，类型学采用隐变量模型。我们将这些信号统一为一个稳健、任务无关的复合距离。在多个零样本迁移基准测试中，当距离类型与任务相关时，我们的表示显著提升迁移性能；而复合距离在大多数任务中均带来性能增益。
原文摘要 · Abstract (English)
Existing linguistic knowledge bases such as URIEL+ provide valuable geographic, genetic and typological distances for cross-lingual transfer but suffer from two key limitations. First, their one-size-fits-all vector representations are ill-suited to the diverse structures of linguistic data. Second, they lack a principled method for aggregating these signals into a single, comprehensive score. In this paper, we address these gaps by introducing a framework for type-matched language distances. We propose novel, structure-aware representations for each distance type: speaker-weighted distributions for geography, hyperbolic embeddings for genealogy, and a latent variables model for typology. We unify these signals into a robust, task-agnostic composite distance. Across multiple zero-shot transfer benchmarks, we demonstrate that our representations significantly improve transfer performance when the distance type is relevant to the task, while our composite distance yields gains in most tasks.
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