From Spatial Relations to Spatial Configurations

• URL: http://arxiv.org/abs/2007.09557v1
• Date: Sun, 19 Jul 2020 02:11:53 GMT
• Title: From Spatial Relations to Spatial Configurations
• Authors: Soham Dan, Parisa Kordjamshidi, Julia Bonn, Archna Bhatia, Jon Cai, Martha Palmer, Dan Roth
• Abstract summary: spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning. We show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics.
• Score: 64.21025426604274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial Reasoning from language is essential for natural language understanding. Supporting it requires a representation scheme that can capture spatial phenomena encountered in language as well as in images and videos. Existing spatial representations are not sufficient for describing spatial configurations used in complex tasks. This paper extends the capabilities of existing spatial representation languages and increases coverage of the semantic aspects that are needed to ground the spatial meaning of natural language text in the world. Our spatial relation language is able to represent a large, comprehensive set of spatial concepts crucial for reasoning and is designed to support the composition of static and dynamic spatial configurations. We integrate this language with the Abstract Meaning Representation(AMR) annotation schema and present a corpus annotated by this extended AMR. To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.

Related papers

• LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts [107.11267074981905]
  We propose a semantically controllable layout-AWare diffusion model, termed LAW-Diffusion. We show that LAW-Diffusion yields the state-of-the-art generative performance, especially with coherent object relations.
  arXiv  Detail & Related papers  (2023-08-13T08:06:18Z)
• Latent Topology Induction for Understanding Contextualized Representations [84.7918739062235]
  We study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations.
  arXiv  Detail & Related papers  (2022-06-03T11:22:48Z)
• Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses [62.197912623223964]
  We show a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. This suggests that the embedding captures some part of the brain's natural language representation structure.
  arXiv  Detail & Related papers  (2021-06-09T22:59:12Z)
• High-dimensional distributed semantic spaces for utterances [0.2907403645801429]
  This paper describes a model for high-dimensional representation for utterance and text level data. It is based on a mathematically principled and behaviourally plausible approach to representing linguistic information. The paper shows how the implemented model is able to represent a broad range of linguistic features in a common integral framework of fixed dimensionality.
  arXiv  Detail & Related papers  (2021-04-01T12:09:47Z)
• Spatial Language Understanding for Object Search in Partially Observed Cityscale Environments [21.528770932332474]
  We introduce the spatial language observation space and formulate a model under the framework of Partially Observable Markov Decision Process (POMDP) We propose a convolutional neural network model that learns to predict the language provider's relative frame of reference (FoR) given environment context. We demonstrate the generalizability of our FoR prediction model and object search system through cross-validation over areas of five cities, each with a 40,000m$2$ footprint.
  arXiv  Detail & Related papers  (2020-12-04T16:27:59Z)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Understanding Spatial Relations through Multiple Modalities [78.07328342973611]
  spatial relations between objects can either be explicit -- expressed as spatial prepositions, or implicit -- expressed by spatial verbs such as moving, walking, shifting, etc. We introduce the task of inferring implicit and explicit spatial relations between two entities in an image. We design a model that uses both textual and visual information to predict the spatial relations, making use of both positional and size information of objects and image embeddings.
  arXiv  Detail & Related papers  (2020-07-19T01:35:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.