探索共享跨语言空间中的对齐
Exploring Alignment in Shared Cross-lingual Spaces
AI总结：
本研究通过聚类方法分析多语言模型潜在空间中的概念对齐与重叠，提出CA和CO指标，发现深层网络和微调增强跨语言对齐，并解释零样本能力来源。
摘要：
尽管多语言嵌入在捕捉不同语言的细微差别方面表现出非凡的能力，但关于多语言嵌入中语言之间对齐程度的问题仍然存在。受神经语言模型中高维表示研究的启发，我们采用聚类方法来揭示多语言模型中的潜在概念。我们的分析重点在于量化潜在空间中这些概念在不同语言之间的对齐和重叠。为此，我们引入了两个指标CA和CO，旨在量化这些方面，从而更深入地探索多语言嵌入。我们的研究涵盖三个多语言模型（mT5、mBERT和XLM-R）和三个下游任务（机器翻译、命名实体识别和情感分析）。我们分析的主要发现包括：i）网络中较深的层由于存在与语言无关的概念而表现出更强的跨语言对齐；ii）模型的微调增强了潜在空间内的对齐；iii）这种任务特定的校准有助于解释模型中零样本能力的出现。代码可在https://github.com/baselmousi/multilingual-latent-concepts获取。
英文摘要：
Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embeddings. Drawing inspiration from research on high-dimensional representations in neural language models, we employ clustering to uncover latent concepts within multilingual models. Our analysis focuses on quantifying the \textit{alignment} and \textit{overlap} of these concepts across various languages within the latent space. To this end, we introduce two metrics \CA{} and \CO{} aimed at quantifying these aspects, enabling a deeper exploration of multilingual embeddings. Our study encompasses three multilingual models (\texttt{mT5}, \texttt{mBERT}, and \texttt{XLM-R}) and three downstream tasks (Machine Translation, Named Entity Recognition, and Sentiment Analysis). Key findings from our analysis include: i) deeper layers in the network demonstrate increased cross-lingual \textit{alignment} due to the presence of language-agnostic concepts, ii) fine-tuning of the models enhances \textit{alignment} within the latent space, and iii) such task-specific calibration helps in explaining the emergence of zero-shot capabilities in the models.\footnote{The code is available at \url{https://github.com/baselmousi/multilingual-latent-concepts}}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
