Phonological Layers of Meaning: A Computational Exploration of Sound Iconicity

Andrea Gregor de Varda, C. Strapparava
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Abstract

The present paper aims to investigate the nature and the extent of cross-linguistic phonosemantic correspondences within a computational framework. An LSTMbased Recurrent Neural Network is trained to associate the phonetic representation of a word, encoded as a sequence of feature vectors, to its corresponding semantic representation in a multilingual vector space. The processing network is tested, without further training, in a language that does not appear in the training set. The performance of the multilingual model is compared with a monolingual upper bound and a randomized baseline. After the quantitative evaluation of its performance, a qualitative analysis is carried out on the network’s most effective predictions, showing an inhomogeneous distribution of phonosemantic information in the lexicon, influenced by semantic, syntactic, and pragmatic factors.
语音意义层:语音象似性的计算探索
本文的目的是在计算框架内研究跨语言语音对应的性质和程度。基于lstm的递归神经网络被训练将一个词的语音表示(编码为一系列特征向量)与多语言向量空间中相应的语义表示相关联。处理网络在没有进一步训练的情况下,用一种没有出现在训练集中的语言进行测试。将多语言模型的性能与单语言上界和随机基线进行比较。在对其性能进行定量评估之后,对网络最有效的预测进行定性分析,显示出词汇中语音信息的不均匀分布,受语义、句法和语用因素的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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