Interactive-predictive neural multimodal systemsThe research leading to these results has received funding from MINECO under grant TIN2015-70924-C2-1-R, IDIFEDER/2018/025 ”Sistemas de fabricación inteligentes para la industria 4.0” Actuación cofinanciada por la Unión Europea a través del Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) de la Comunitat Valenciana 2014-2020, and from the European Commission under grant H2020, reference 825111. We also acknowledge NVIDIA Corporation for the donation of GPUs used in this work.
Table 1: Figures of the different datasets. M denotes millions of elements. The column #References indicates the number of different references per sample. ⋆ denotes a variable number of references. In this case, we report the average references per sample.
['Task', 'Dataset', '#Samples Training', '#Samples Validation', '#Samples Test', '#References']
We evaluate our interactive-predictive framework in six different scenarios, involving three tasks and two different datasets per task. The tasks under study are: It is worth to note that the Transformer model only outperformed the RNN-based systems in the case of machine translation. This model is more data-eager than RNN systems. Many of the recent advances yielded with this architecture leverage huge data collections (e.g. Radford et al. Hence, the Transformer model only was fully exploited in the machine translation case.