A new dataset and model for learning to understand navigational instructions
Table 5: Results on the generated fixed data. The average and ensemble scores were obtained by using 10 models for each architecture.
['[EMPTY]', 'Dev Avg.', 'Dev Ens.', 'Test Avg.', 'Test Ens.']
We split this data into train (70%), dev (15%) and test (15%) sets while preserving the proportion of each subtask. The grid based model is significantly better than the B.O.F baseline. The L.O baseline reaches an accuracy higher than the proportion of the language only data. One reason might be that since the model uses the previous action as the input of the decoder, it captures some action sequence patterns.