Contents
Table 6.6: Comparison between different n-gram models, on Flickr30K data. 2g is bi-gram, 3g is trigram and tk3 is combination of tri-gram and skip-gram features. In all case size of input vocabulary is 50,000 and all rest model parameters are similar. The bold figures show best results in the specific row. nhu is the hidden layer in the textual model, while nemb is the size of embedding dimension.
['[BOLD] n-gram model comparison Models', '[BOLD] n-gram model comparison Evaluation Metric', '[BOLD] n-gram model comparison Evaluation Metric', '[BOLD] n-gram model comparison Evaluation Metric', '[BOLD] n-gram model comparison Evaluation Metric', '[BOLD] n-gram model comparison Evaluation Metric', '[BOLD] n-gram model comparison Evaluation Metric']
We replaced the BoW textual model with n-gram model. N-gram models conserve some sentence-order information and so theoretically they act as better features. In our experiments we tried bigrams (2g), trigrams (3g) and trigrams + skip grams (tk3). In all these cases the vocabulary size is fixed to 50,000 which is the size of input layer of textual model MLP.