@inproceedings{zang-etal-2018-translating,
title = "Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation",
author = "Zang, Xiaoxue and
Pokle, Ashwini and
V{\'a}zquez, Marynel and
Chen, Kevin and
Niebles, Juan Carlos and
Soto, Alvaro and
Savarese, Silvio",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1286",
doi = "10.18653/v1/D18-1286",
pages = "2657--2666",
abstract = "We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model{'}s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.",
}
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<abstract>We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model’s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.</abstract>
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%0 Conference Proceedings
%T Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
%A Zang, Xiaoxue
%A Pokle, Ashwini
%A Vázquez, Marynel
%A Chen, Kevin
%A Niebles, Juan Carlos
%A Soto, Alvaro
%A Savarese, Silvio
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F zang-etal-2018-translating
%X We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model’s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.
%R 10.18653/v1/D18-1286
%U https://aclanthology.org/D18-1286
%U https://doi.org/10.18653/v1/D18-1286
%P 2657-2666
Markdown (Informal)
[Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation](https://aclanthology.org/D18-1286) (Zang et al., EMNLP 2018)
ACL