@inproceedings{han-etal-2026-advancing,
title = "Advancing {D}ial{N}av through Automatic Embodied Dialog Augmentation",
author = "Han, Leekyeung and
Jung, Sangwon and
Min, Hyunji and
Jeong, Jinseong and
Kim, Minyoung and
Seo, Paul Hongsuck",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1972/",
pages = "44876--44904",
ISBN = "979-8-89176-459-0",
abstract = "For embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness. While DialNav provides a framework for holistic evaluation of the dialog{--}execution loop in photorealistic indoor navigation, its performance remains limited by a critical scarcity of training data (2K episodes) To address this, we propose an automatic generation pipeline and generate the RAINbow dataset, a large-scale training dataset with 238K episodes for DialNav. Then, we introduce two additional complementary advances to unlock the data{'}s full potential: (1) a navigation training scheme to align the navigation training with the dynamic dialog-navigation loop, and (2) a localization model that leverages VLN knowledge. By combining these complementary solutions, our model substantially outperforms the success rate of the baseline on both Val Seen (58.24, +89{\%}) and Val Unseen (29.05, +100{\%}) splits, establishing a new state of the art. We will publicly release our dataset, code, and models."
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<abstract>For embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness. While DialNav provides a framework for holistic evaluation of the dialog–execution loop in photorealistic indoor navigation, its performance remains limited by a critical scarcity of training data (2K episodes) To address this, we propose an automatic generation pipeline and generate the RAINbow dataset, a large-scale training dataset with 238K episodes for DialNav. Then, we introduce two additional complementary advances to unlock the data’s full potential: (1) a navigation training scheme to align the navigation training with the dynamic dialog-navigation loop, and (2) a localization model that leverages VLN knowledge. By combining these complementary solutions, our model substantially outperforms the success rate of the baseline on both Val Seen (58.24, +89%) and Val Unseen (29.05, +100%) splits, establishing a new state of the art. We will publicly release our dataset, code, and models.</abstract>
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%0 Conference Proceedings
%T Advancing DialNav through Automatic Embodied Dialog Augmentation
%A Han, Leekyeung
%A Jung, Sangwon
%A Min, Hyunji
%A Jeong, Jinseong
%A Kim, Minyoung
%A Seo, Paul Hongsuck
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F han-etal-2026-advancing
%X For embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness. While DialNav provides a framework for holistic evaluation of the dialog–execution loop in photorealistic indoor navigation, its performance remains limited by a critical scarcity of training data (2K episodes) To address this, we propose an automatic generation pipeline and generate the RAINbow dataset, a large-scale training dataset with 238K episodes for DialNav. Then, we introduce two additional complementary advances to unlock the data’s full potential: (1) a navigation training scheme to align the navigation training with the dynamic dialog-navigation loop, and (2) a localization model that leverages VLN knowledge. By combining these complementary solutions, our model substantially outperforms the success rate of the baseline on both Val Seen (58.24, +89%) and Val Unseen (29.05, +100%) splits, establishing a new state of the art. We will publicly release our dataset, code, and models.
%U https://aclanthology.org/2026.emnlp-main.1972/
%P 44876-44904
Markdown (Informal)
[Advancing DialNav through Automatic Embodied Dialog Augmentation](https://aclanthology.org/2026.emnlp-main.1972/) (Han et al., EMNLP 2026)
ACL
- Leekyeung Han, Sangwon Jung, Hyunji Min, Jinseong Jeong, Minyoung Kim, and Paul Hongsuck Seo. 2026. Advancing DialNav through Automatic Embodied Dialog Augmentation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 44876–44904, Budapest, Hungary. Association for Computational Linguistics.