Effects of Word Embeddings on Neural Network-based Pitch Accent Detection
Table 6: Stopword accuracy (%) using unigram embeddings and 10 bottleneck features.
['[EMPTY]', 'BURNC', 'BDC', 'LeaP']
In word2vec, stopwords are represented as OOV with a zero vector, wheras in GloVe, they are part of the vocabulary. On the LeaP and BDC datasets, we observe better performance on these words with both types of embeddings. It appears that stopwords are more likely to be labelled correctly (which is usually not accented) when there is some information about the word identity available, regardless of how they are represented as vectors. The results on BURNC do not show a clear pattern. Overall, these numbers appear to be reflective of the overall increase in performance obtained by adding the lexical information.