Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions
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| Format: | Preprint |
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2024
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| _version_ | 1866913456882450432 |
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| author | Paz-Argaman, Tzuf Kulkarni, Sayali Palowitch, John Baldridge, Jason Tsarfaty, Reut |
| author_facet | Paz-Argaman, Tzuf Kulkarni, Sayali Palowitch, John Baldridge, Jason Tsarfaty, Reut |
| contents | When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquired knowledge on textual descriptions. Current navigation studies concentrate on egocentric local descriptions (e.g., `it will be on your right') that require reasoning over the agent's local perception. These instructions are typically given as a sequence of steps, with each action-step explicitly mentioning and being followed by a landmark that the agent can use to verify they are on the right path (e.g., `turn right and then you will see...'). In contrast, descriptions based on knowledge acquired through a map provide a complete view of the environment and capture its overall structure. These instructions (e.g., `it is south of Central Park and a block north of a police station') are typically non-sequential, contain allocentric relations, with multiple spatial relations and implicit actions, without any explicit verification. This paper introduces the Rendezvous (RVS) task and dataset, which includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. Our analysis reveals that RVS exhibits a richer use of spatial allocentric relations, and requires resolving more spatial relations simultaneously compared to previous text-based navigation benchmarks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_16364 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions Paz-Argaman, Tzuf Kulkarni, Sayali Palowitch, John Baldridge, Jason Tsarfaty, Reut Computation and Language Machine Learning Multimedia When communicating routes in natural language, the concept of acquired spatial knowledge is crucial for geographic information retrieval (GIR) and in spatial cognitive research. However, NLP navigation studies often overlook the impact of such acquired knowledge on textual descriptions. Current navigation studies concentrate on egocentric local descriptions (e.g., `it will be on your right') that require reasoning over the agent's local perception. These instructions are typically given as a sequence of steps, with each action-step explicitly mentioning and being followed by a landmark that the agent can use to verify they are on the right path (e.g., `turn right and then you will see...'). In contrast, descriptions based on knowledge acquired through a map provide a complete view of the environment and capture its overall structure. These instructions (e.g., `it is south of Central Park and a block north of a police station') are typically non-sequential, contain allocentric relations, with multiple spatial relations and implicit actions, without any explicit verification. This paper introduces the Rendezvous (RVS) task and dataset, which includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. Our analysis reveals that RVS exhibits a richer use of spatial allocentric relations, and requires resolving more spatial relations simultaneously compared to previous text-based navigation benchmarks. |
| title | Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions |
| topic | Computation and Language Machine Learning Multimedia |
| url | https://arxiv.org/abs/2402.16364 |