Listen, Attend, and Walk: Neural Mappingof Navigational Instructions to Action Sequences
Table 1: Overall accuracy (state-of-the-art in bold)
['Method', 'Single-sent', 'Multi-sent']
We first investigate the ability to navigate to the intended destination for a given natural language instruction. We report two statistics with our model (vDev and vTest) in order to directly compare with existing work. Our multi-sentence accuracy, which is working with a really small amount of training data (a few hundred paragraph pairs), is competitive with state-of-the-art and outperforms several previous methods that use additional, specialized resources in the form of semantic parsers, logical-form lexicons, and re-rankers. We note that our model yields good results using only greedy search (beam width of one). For vDev, we achieve 68.05 on single-sentence and 23.93 on multi-sentence, while for vTest, we get 70.56 on single-sentence and 27.91 on multi-sentence.