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Learning Semantic Maps from Natural Language Descriptions
2013
Robotics: Science and Systems IX
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This paper proposes an algorithm that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. ...
This semantic graph provides a common framework in which we integrate concepts from natural language descriptions (e.g., labels and spatial relations) with metric observations from low-level sensors. ...
CONCLUSION We described a semantic mapping algorithm enabling robots to efficiently learn metrically accurate semantic maps from natural language descriptions. ...
doi:10.15607/rss.2013.ix.004
dblp:conf/rss/WalterHHTT13
fatcat:zcaa34ydgbhh3h7vpl3ubmcmnm
A framework for learning semantic maps from grounded natural language descriptions
2014
The international journal of robotics research
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This paper describes a framework that enables robots to efficiently learn human-centric models of their environment from natural language descriptions. ...
We evaluate the algorithm's ability to learn human-centric maps of several different environments and analyze the knowledge inferred from language and the utility of the learned maps. ...
Semantic Accuracy
Discussion
Learning from Allocentric, Anticipatory Language A contribution of our work is the use of natural language descriptions to produce consistent semantic maps from spatial ...
doi:10.1177/0278364914537359
fatcat:2g5u2mda4nctjjtwrjdtiyicke
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
[article]
2017
arXiv
pre-print
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The key component of DeepAM is based on the multimodal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data. ...
Existing approaches mine API mappings from projects that have corresponding versions in two languages. ...
natural language description. ...
arXiv:1704.07734v1
fatcat:4d5y4lnhwjbq5pzi477r6ngk5i
DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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The key component of DeepAM is based on the multi-modal sequence to sequence learning architecture that aims to learn joint semantic representations of bilingual API sequences from big source code data ...
Existing approaches mine API mappings from projects that have corresponding versions in two languages. ...
translate from the semantic representations V to corresponding natural language descriptions D. ...
doi:10.24963/ijcai.2017/514
dblp:conf/ijcai/GuZZ017
fatcat:awd6oa4atnaoziu3ezz6nwrodu
E-Learning resource reuse, based on bilingual ontology annotation and ontology mapping
2019
International Journal of Advanced Computer Research
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standards, etc.), used for semantic description of e-Learning courses. ...
natural language [16] . ...
doi:10.19101/ijacr.2019.940101
fatcat:nzumk4kc4bfzhdhoq2wgjeixdu
Rich Semantics Improve Few-shot Learning
[article]
2021
arXiv
pre-print
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Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). ...
This enables us to learn generalizable concepts from very limited visual examples. ...
[1] use the language descriptions during the pertaining stage in FSL to learn natural task structure. ...
arXiv:2104.12709v2
fatcat:ttqiek7zgbfsbjulzqpgzatrze
Hybrid Attention Network for Language-Based Person Search
2020
Sensors
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Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. ...
It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively ...
Context Information Extraction Using BiLSTM The descriptions of persons from different witnesses are in the form of natural language. ...
doi:10.3390/s20185279
pmid:32942720
pmcid:PMC7570628
fatcat:hrqu7lz4frg4vdvoicory4vm6e
Language is Power: Representing States Using Natural Language in Reinforcement Learning
[article]
2020
arXiv
pre-print
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natural language representations for reinforcement learning. ...
., raw visual input), we propose to represent the state using natural language. Language can represent complex scenarios and concepts, making it a favorable candidate for representation. ...
ViZDoom's semantic segmentation maps were used as the core element for generating our natural language descriptions. ...
arXiv:1910.02789v2
fatcat:nsoh4gxudzczrcyeblalirtqui
PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation
2019
Applied Sciences
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Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. ...
We employ neural networks for natural-language-based scene understanding to share environmental information among robots. ...
On language description part, a GCN extracts features from the graph map. The extracted graph feature is concatenated with a word and feed into the RNN as input. ...
doi:10.3390/app9183789
fatcat:lsfmciw4rvaxvlpwmznuodrqte
Robots That Use Language
2020
Annual Review of Control Robotics and Autonomous Systems
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This article surveys the use of natural language in robotics from a robotics point of view. ...
This problem differs from other natural language processing domains due to the need to ground the language to noisy percepts and physical actions. ...
(158) incorporates information from language into a semantic map of the environment. ...
doi:10.1146/annurev-control-101119-071628
fatcat:gljaeee7pvhb3ohuur3agdvvyq
Learning to interpret spatial natural language in terms of qualitative spatial relations*
[chapter]
2013
Representing Space in Cognition
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In this chapter, we apply formal models and machine learning techniques to map spatial semantics in natural language to qualitative spatial representations. ...
, and (ii) we map the extracted parts that result from the first task to qualitative spatial representations. ...
4 Machine Learning: From Spatial Language to RCC In this section, we present the machine learning task for mapping linguistic spatial features extracted from natural language to spatial calculi (see again ...
doi:10.1093/acprof:oso/9780199679911.003.0007
fatcat:37yp4icujjgm3eypqywb2qruei
Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus
[article]
2022
arXiv
pre-print
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Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic concepts and semantics ...
Semantic code search is the task of retrieving relevant code snippet given a natural language query. ...
In our work, code token vectors are learnt independently for each language, and we learn a linear map W lang for each language and use it to project the code token vectors to a same semantic space, as ...
arXiv:2201.11313v1
fatcat:er5p53ejsbenjaywdppdrhtcyu
Zero-shot Learning of Classifiers from Natural Language Quantification
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Humans can efficiently learn new concepts using language. ...
We use semantic parsing to map explanations to probabilistic assertions grounded in latent class labels and observed attributes of unlabeled data, and leverage the differential semantics of linguistic ...
First, we map the set of natural language explanations of a concept to logical forms Figure 2: Our approach to Zero-shot learning from Language. ...
doi:10.18653/v1/p18-1029
dblp:conf/acl/MitchellSL18
fatcat:fuhmnmaqzbghnjdat3w4ofz3jm
An Open Vocabulary Semantic Parser for End-User Programming Using Natural Language
2018
2018 IEEE 12th International Conference on Semantic Computing (ICSC)
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In this paper, we propose a semantic parsing approach to map natural language commands to actions from a large and heterogeneous frame set trained under a small set of annotated data. ...
The ability to automatically interpret natural language commands and actions has the potential of freeing up endusers to interact with software artefacts without the syntactic, vocabulary and formal constraints ...
MAPPING NATURAL LANGUAGE COMMANDS TO ACTION FRAMES The semantic parsing of natural language commands consists of mapping a natural language command to a formal function representation from a knowledge ...
doi:10.1109/icsc.2018.00020
dblp:conf/semco/SalesFH18
fatcat:tf3eo5qfkvc3hp5y6tmg5gzmpe
Imitation learning for natural language direction following through unknown environments
2013
2013 IEEE International Conference on Robotics and Automation
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However, natural language direction following through unknown environments requires understanding the meaning of language, using a partial semantic world model to generate actions in the world, and reasoning ...
We address the problem of robots following natural language directions through complex unknown environments. ...
We have appreciated valuable discussions with Stéphane Ross and Drew Bagnell, as well as the comments from the anonymous reviewers. ...
doi:10.1109/icra.2013.6630702
dblp:conf/icra/DuvalletKS13
fatcat:b7olkax2tnd6jbdanc4lbx6dpq
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