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We introduce a novel speaker model \textsc{Kefa} for navigation instructiongeneration. The existing speaker models in Vision-and-Language Navigationsuffer from the large domain gap of vision features between differentenvironments and insufficient temporal grounding capability. To address thechallenges, we propose a Knowledge Refinement Module to enhance the featurerepresentation with external knowledge facts, and an Adaptive TemporalAlignment method to enforce fine-grained alignment between the generatedinstructions and the observation sequences. Moreover, we propose a new metricSPICE-D for navigation instruction evaluation, which is aware of thecorrectness of direction phrases. The experimental results on R2R and UrbanWalkdatasets show that the proposed KEFA speaker achieves state-of-the-artinstruction generation performance for both indoor and outdoor scenes.

Kefa: A Knowledge Enhanced and Fine-grained Aligned Speaker for Navigation Instruction Generation