Paper:
In order to make Visual Question Answering (VQA) explainable, previous studies not only visualize the attended region of a VQA model but also generate textual explanations for its answers. However, when the model's answer is 'no,' existing methods have difficulty in revealing detailed arguments that lead to that answer. In addition, previous methods are insufficient to provide logical bases, when the question requires common sense to answer. In this paper, we propose a novel textual explanation method to overcome the aforementioned limitations. First, we extract keywords that are essential to infer an answer from a question. Second, for a pre-trained explanation generator, we utilize a novel Variable-Constrained Beam Search (VCBS) algorithm to generate phrases that best describes the relationship between keywords in images. Then, we complete an explanation by feeding the phrase to the generator. Furthermore, if the answer to the question is "yes" or "no," we apply Natural Langauge Inference (NLI) to identify whether contents of the question can be inferred from the explanation using common sense. Our user study, conducted in Amazon Mechanical Turk (MTurk), shows that our proposed method generates more reliable explanations compared to the previous methods. Moreover, by modifying the VQA model's answer through the output of the NLI model, we show that VQA performance increases by 1.1% from the original model.

Review:
Interesting results and analysis but lack of novelty and details. 
This paper proposed a novel text explanation method which extracts keywords that are essential to infer an answer from question. The authors proposed VCBS based on CBS and use Natural language inference to identify the entailments when the answer is yes or no. Experiment results show better mean opinion score 100 random samples results compared with the previous method and better VQA binary classifications when flipping based on the NLI results. 
Different from [Park et. al. 2018], who collected the explanations, this paper directly use the coco captions dataset as the source for the explanations. One of my major concern about this paper is it actually generate relevant captions with respect to the question and answer instead of real explanations. It's true that correlated caption can sometimes serve as the explanation when they cover the same concept coincidently but, we can not use these captions to explain the reasoning process of VQA. 
The technique novelty of the proposed paper is also limited, the major novelty is the VCBS, which seems very similar to CBS. The only difference is VCBS adds relaxed parameters, which seems no technique novelty. Most annotations in Algorithms 1 is also not explained, making the readers hard to follow the actual content. The proposed model, although tied the vqa words with the explanation words, it suffers the same problems as PJ-X model, which didn't consider the VQA attention at all. 
The experiment is also weak, considering the results is conduct on 100 samples, there might be significant variance. It's interesting the compared approach is learned based on a different dataset, which makes the results harder to compare. The NLI model results are interesting but for a more fair comparison, I would expect the proposed method compare with a model trained with VQA and coco caption dataset, such as VQA-E.