Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Table 3: Evaluation of Textual Justifications. Evaluated automatic metrics: BLEU-4 (B), METEOR (M), ROUGE (R), CIDEr (C) and SPICE (S). Reference sentence for human and automatic evaluation is always an explanation. All in %. Our proposed model compares favorably to baselines.
['[EMPTY]', 'Train- ing', 'Att. for', 'Answer Condi-', 'VQA-X Automatic evaluation', 'VQA-X Automatic evaluation', 'VQA-X Automatic evaluation', 'VQA-X Automatic evaluation', 'VQA-X Automatic evaluation', 'VQA-X Human', 'ACT-X Automatic evaluation', 'ACT-X Automatic evaluation', 'ACT-X Automatic evaluation', 'ACT-X Automatic evaluation', 'ACT-X Automatic evaluation', 'ACT-X Human']
“Ours” model significantly improves “Ours with description” model by a large margin on both datasets which is expected as descriptions are not collected for the task of generating explanations, it demonstrates the necessity for explanation datasets to build explanation models. This further confirms that our new datasets with ground truth explanations are important for textual justification generation.