Learning Convolutional Text Representations for Visual Question Answering
Table 4: Comparison of results between deep learning models and fastText.
['[BOLD] Models', '[BOLD] Accuracy']
This result contradicts the common belief that deep learning models can learn better representations. It has been conjectured that the simple text classification task may not be the right one to evaluate text representation methods. Given the higher requirements for text understanding in VQA, we compare these models in VQA. In addition to the original fastText model (“fastText (word)”), which averages word embedding vectors to obtain sentence representations, we also explore fastText (“fastText (char+word)”) with character-based vocabulary. We can see the performance gap between deep learning models and fastText. Clearly, it demonstrates the complexity of VQA tasks and the power of deep learning.