@inproceedings{paz-argaman-tsarfaty-2019-run,
title = "{RUN} through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation",
author = "Paz-Argaman, Tzuf and
Tsarfaty, Reut",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1681",
doi = "10.18653/v1/D19-1681",
pages = "6449--6455",
abstract = "Following navigation instructions in natural language (NL) requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Previous work on map-based NL navigation relied on small artificial worlds with a fixed set of entities known in advance. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting NL navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We then empirically study which aspects of a neural architecture are important for the RUN success, and empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.",
}
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%0 Conference Proceedings
%T RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation
%A Paz-Argaman, Tzuf
%A Tsarfaty, Reut
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F paz-argaman-tsarfaty-2019-run
%X Following navigation instructions in natural language (NL) requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Previous work on map-based NL navigation relied on small artificial worlds with a fixed set of entities known in advance. Here we introduce the Realistic Urban Navigation (RUN) task, aimed at interpreting NL navigation instructions based on a real, dense, urban map. Using Amazon Mechanical Turk, we collected a dataset of 2515 instructions aligned with actual routes over three regions of Manhattan. We then empirically study which aspects of a neural architecture are important for the RUN success, and empirically show that entity abstraction, attention over words and worlds, and a constantly updating world-state, significantly contribute to task accuracy.
%R 10.18653/v1/D19-1681
%U https://aclanthology.org/D19-1681
%U https://doi.org/10.18653/v1/D19-1681
%P 6449-6455
Markdown (Informal)
[RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation](https://aclanthology.org/D19-1681) (Paz-Argaman & Tsarfaty, EMNLP-IJCNLP 2019)
ACL