Besides the NLVR corpus with a focus on complex and natural compositional language (Suhr et al., 2017), other useful visual reasoning datasets have been proposed for navigation and assembly tasks (MacMahon et al., 2006;Bisk et al., 2016), as well as for visual Q&A tasks which focus more on complex real-world images (Antol et al., 2015;Johnson et al., 2017).
for the NLVR dataset, previous models have incorporated property-and count-based features of the objects and the language (Suhr et al., 2017), or extra semantic parsing (logical form) annotations (Goldman et al., 2017) -we focus on end-toend models for this visual reasoning task.