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        <title>Exploring Intra and Inter-language Consistency in Embeddings with ICA</title>
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    <name type="personal">
        <namePart type="given">Hitomi</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Word embeddings represent words as multidimensional real vectors, facilitating data analysis and processing, but are often challenging to interpret. Independent Component Analysis (ICA) creates clearer semantic axes by identifying independent key features. Previous research has shown ICA’s potential to reveal universal semantic axes across languages. However, it lacked verification of the consistency of independent components within and across languages. We investigated the consistency of semantic axes in two ways: both within a single language and across multiple languages. We first probed into intra-language consistency, focusing on the reproducibility of axes by performing ICA multiple times and clustering the outcomes. Then, we statistically examined inter-language consistency by verifying those axes’ correspondences using statistical tests. We newly applied statistical methods to establish a robust framework that ensures the reliability and universality of semantic axes.</abstract>
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%0 Conference Proceedings
%T Exploring Intra and Inter-language Consistency in Embeddings with ICA
%A Li, Rongzhi
%A Matsuda, Takeru
%A Yanaka, Hitomi
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-exploring-intra
%X Word embeddings represent words as multidimensional real vectors, facilitating data analysis and processing, but are often challenging to interpret. Independent Component Analysis (ICA) creates clearer semantic axes by identifying independent key features. Previous research has shown ICA’s potential to reveal universal semantic axes across languages. However, it lacked verification of the consistency of independent components within and across languages. We investigated the consistency of semantic axes in two ways: both within a single language and across multiple languages. We first probed into intra-language consistency, focusing on the reproducibility of axes by performing ICA multiple times and clustering the outcomes. Then, we statistically examined inter-language consistency by verifying those axes’ correspondences using statistical tests. We newly applied statistical methods to establish a robust framework that ensures the reliability and universality of semantic axes.
%U https://aclanthology.org/2024.emnlp-main.1065
%P 19104-19111

Markdown (Informal)

[Exploring Intra and Inter-language Consistency in Embeddings with ICA](https://aclanthology.org/2024.emnlp-main.1065) (Li et al., EMNLP 2024)

• Exploring Intra and Inter-language Consistency in Embeddings with ICA (Li et al., EMNLP 2024)

ACL

• Rongzhi Li, Takeru Matsuda, and Hitomi Yanaka. 2024. Exploring Intra and Inter-language Consistency in Embeddings with ICA. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 19104–19111, Miami, Florida, USA. Association for Computational Linguistics.