迈向对多语言端到端语音翻译的深入理解
Towards a Deep Understanding of Multilingual End-to-End Speech Translation
AI总结：
本文利用SVCCA分析22种语言的多语言端到端语音翻译模型表示，发现数据有限时语言相似性失效、良好对齐数据可提升翻译质量且编码器表示能预测语音特征，并提出结合低资源与相关高资源语言的更有效方法。
摘要：
在本文中，我们采用奇异值典型相关分析（SVCCA）来分析一个在22种语言上训练的多语言端到端语音翻译模型所学习到的表示。SVCCA使我们能够估计跨语言和跨层的表示相似性，从而加深我们对多语言语音翻译功能及其与多语言神经机器翻译潜在联系的理解。该多语言语音翻译模型在CoVoST 2数据集上以所有可能的方向进行训练，我们利用LASER提取平行双文本数据用于SVCCA分析。我们从分析中得出三个主要发现：（I）当特定语言的训练数据有限时，语言相似性在多语言语音翻译中失去其效力。（II）增强的编码器表示和良好对齐的音频-文本数据显著提升翻译质量，在训练数据未受损的情况下超越双语对应模型。（III）多语言语音翻译的编码器表示在语言类型学预测中预测语音特征方面表现出优越性能。基于这些发现，我们提出，解除低资源语言有限数据的约束，并随后将其与语言上相关的高资源语言相结合，可能为多语言端到端语音翻译提供一种更有效的方法。
英文摘要：
In this paper, we employ Singular Value Canonical Correlation Analysis (SVCCA) to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. SVCCA enables us to estimate representational similarity across languages and layers, enhancing our understanding of the functionality of multilingual speech translation and its potential connection to multilingual neural machine translation. The multilingual speech translation model is trained on the CoVoST 2 dataset in all possible directions, and we utilize LASER to extract parallel bitext data for SVCCA analysis. We derive three major findings from our analysis: (I) Linguistic similarity loses its efficacy in multilingual speech translation when the training data for a specific language is limited. (II) Enhanced encoder representations and well-aligned audio-text data significantly improve translation quality, surpassing the bilingual counterparts when the training data is not compromised. (III) The encoder representations of multilingual speech translation demonstrate superior performance in predicting phonetic features in linguistic typology prediction. With these findings, we propose that releasing the constraint of limited data for low-resource languages and subsequently combining them with linguistically related high-resource languages could offer a more effective approach for multilingual end-to-end speech translation.
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。
