We take an end-to-end, sequence-to-sequence learning approach to the task of following natural language route instructions, i.e., mapping natural language instructions to action sequences. Our model is a bidirectional, alignment-based, long short-term memory recurrent neural network (LSTM RNN) that encodes the freeform navigational instruction sentence and the corresponding representation of the environment state. We propose a multi-level aligner as part of our network that empowers the model to focus on salient sentence "regions", using both high-and low-level input representations. This alignment-based LSTM then decodes the learned representation to obtain the inferred action sequence. Adding bidirectionality to the network helps further. In contrast to existing methods, our model uses no additional information or resources about the task or language (e.g., parsers or seed lexicons) and still achieves the best results reported to-date on a benchmark single-sentence dataset and gives competitive results for the limited-training multi-sentence setting. We evaluate our model through a series of ablation studies that elucidate the contributions of the primary components of our model.
REF map navigational instructions to action sequences by representing the state using bag of word features.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
ArXiv
ArXiv
Computer Science