2021/05/14 by Yue Zhang, Quan Guo, Zhang, Yue +3 · 2 citations
Computer Science · #Computation and Language (cs.CL) #Constraint Satisfaction and Optimization #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2105.06839
openalex publication_date 2021/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We deal with the navigation problem where the agent follows natural language instructions while observing the environment. Focusing on language understanding, we show the importance of spatial semantics in grounding navigation instructions into visual perceptions. We propose a neural agent that uses the elements of spatial configurations and investigate their influence on the navigation agent's reasoning ability. Moreover, we model the sequential execution order and align visual objects with spatial configurations in the instruction. Our neural agent improves strong baselines on the seen environments and shows competitive performance on the unseen environments. Additionally, the experimental results demonstrate that explicit modeling of spatial semantic elements in the instructions can improve the grounding and spatial reasoning of the model.