Learning by Asking Questions
Table 5: Accuracy of stacked attention networks (CNN+LSTM+SA) and FiLM on the CLEVR Humans validation set. We show that testing the program on either natural language or a program translated version gives similar accuracy.
['[BOLD] Language', '[BOLD] Model', '[BOLD] Accuracy']
In Section 5.1 of the main paper, we evaluate our models on the CLEVR Humans set. To do so, we train models on the natural language questions in CLEVR (rather than on the question programs), and evaluate the resulting models on CLEVR-Humans. The results in the table show that both models perform similarly and, therefore, which shows that the language-to-program translation does not impact question-answering accuracy.