From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning
Table 3: Spearman’s correlation coefficient between word-word cosine similarity and human similarity judgements. All correlations significant at p <1e−4. Frequent words appear at least 100 times in the training data.
['[EMPTY]', 'All words', 'Frequent words']
In all layers, the cosine similarities between the activation vectors for two words are significantly correlated with human similarity judgements. The strength of the correlation differs considerably between the layers, ranging from 0.09 in the first layer to 0.28 in the highest hidden layer. Correlations for both Word GRU and Word SUM are considerably higher than for Phon GRU. This is expected given that these are word level models with explicit word-embeddings, while Phon GRU builds word representations by forwarding phoneme-level input through several layers of processing. As expected, edit distance and cosine similarity of the activation vectors are negatively correlated: words which are more similar in form are also more similar according to the model.