@inproceedings{cerda-mardini-etal-2020-translating,
title = "Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism",
author = "Cerda-Mardini, Patricio and
Araujo, Vladimir and
Soto, {\'A}lvaro",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.24",
doi = "10.18653/v1/2020.winlp-1.24",
pages = "96--98",
abstract = "We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.",
}
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%0 Conference Proceedings
%T Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
%A Cerda-Mardini, Patricio
%A Araujo, Vladimir
%A Soto, Álvaro
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F cerda-mardini-etal-2020-translating
%X We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.
%R 10.18653/v1/2020.winlp-1.24
%U https://aclanthology.org/2020.winlp-1.24
%U https://doi.org/10.18653/v1/2020.winlp-1.24
%P 96-98
Markdown (Informal)
[Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism](https://aclanthology.org/2020.winlp-1.24) (Cerda-Mardini et al., WiNLP 2020)
ACL