Cross-lingual Similarity of Multilingual Representations Revisited

• URL: http://arxiv.org/abs/2212.01924v1
• Date: Sun, 4 Dec 2022 21:02:07 GMT
• Title: Cross-lingual Similarity of Multilingual Representations Revisited
• Abstract summary: We argue that assumptions of CKA/CCA align poorly with one of the motivating goals of cross-lingual learning analysis. We introduce textitAverage Neuron-Wise Correlation (ANC) as a straightforward alternative that is exempt from the difficulties of CKA/CCA.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Related works used indexes like CKA and variants of CCA to measure the similarity of cross-lingual representations in multilingual language models. In this paper, we argue that assumptions of CKA/CCA align poorly with one of the motivating goals of cross-lingual learning analysis, i.e., explaining zero-shot cross-lingual transfer. We highlight what valuable aspects of cross-lingual similarity these indexes fail to capture and provide a motivating case study \textit{demonstrating the problem empirically}. Then, we introduce \textit{Average Neuron-Wise Correlation (ANC)} as a straightforward alternative that is exempt from the difficulties of CKA/CCA and is good specifically in a cross-lingual context. Finally, we use ANC to construct evidence that the previously introduced ``first align, then predict'' pattern takes place not only in masked language models (MLMs) but also in multilingual models with \textit{causal language modeling} objectives (CLMs). Moreover, we show that the pattern extends to the \textit{scaled versions} of the MLMs and CLMs (up to 85x original mBERT).\footnote{Our code is publicly available at \url{https://github.com/TartuNLP/xsim}}

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