Navigational Instruction Generationas Inverse Reinforcement Learningwith Neural Machine Translation
TABLE I: Aligner ablation results.
['[EMPTY]', 'Full Model', 'No Aligner']
Our model employs an aligner in order to learn to focus on particular CAS tokens that are salient to words in the output instruction. We evaluate the contribution of the aligner by implementing and training an alternative model in which the last encoder hidden state is fed to the decoder. The inclusion of an aligner results in a slight increase in the BLEU score of the generated instructions relative to the human-provided references, and is also useful as a means of visualizing the inner workings of our model (as shown below). Additionally, we empirically find that the aligner improves our model’s ability to learn the association between CAS elements and words in the output, thereby yielding better instructions.