Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation
Table 5: Results on comparative evaluation of relevance on the human-AI image guessing dialogs
['Model', 'Prefered (%)']
Besides a marginal improvement over the RL baseline model and SL pretrained agent in terms of decreased repetition and grammar mistakes , there is a distinct superiority in regards to the relevance to the image caption in the questions generated from our RL agent. Given the image caption bunches of bananas hang on a wall and arranged for sale. , RL-Q and SL-Q-IG ask very general questions that are not related to the caption such as “planes”, “zoo” and “animals”. In comparison, our agent asks high-quality questions regarding the caption that covers “bananas” and “fruits”. These questions help our RL agent obtain useful information to guess the target image. We credit the positive result to the dialog policy, which explores multiple paths to conduct the conversation. The optimal path will involve a set of questions that obtains the maximum information of the target image such that it can construct the best estimation of the target image.