---
title: "Learning to Navigate Unseen Environments: Back Translation"
description: "The authors propose a two-stage training scheme. 41 references, cited by 58 papers here."
authors: ["Hao Tan", "Licheng Yu", "Mohit Bansal"]
year: 2019
venue: "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)"
source: "https://aclanthology.org/N19-1268/"
doi: "10.18653/v1/n19-1268"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/"
---
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# Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

**Authors:** [Hao Tan](https://md.research.elamur.ai/free/en/authors/hao-tan/), [Licheng Yu](https://md.research.elamur.ai/free/en/authors/licheng-yu/) and [Mohit Bansal](https://md.research.elamur.ai/free/en/authors/mohit-bansal/) · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)](https://md.research.elamur.ai/free/en/venues/naacl/) ([NAACL 2019](https://md.research.elamur.ai/free/en/venues/naacl/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N19-1268/) · [PDF](https://aclanthology.org/N19-1268.pdf) · [DOI 10.18653/v1/n19-1268](https://doi.org/10.18653/v1/n19-1268) · [OpenAlex](https://openalex.org/W2926977875)

**Citations:** 289 citations (OpenAlex, 2026-10-14) · [58](https://md.research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses instruction-guided navigation in the Room-to-Room (R2R) task, which is built on real Matterport3D indoor scans. Agents for this task usually lose much of their accuracy in houses they never saw during training. The authors propose a two-stage training scheme.

**Stage 1.** An encoder-decoder agent is trained with a combination of imitation learning and reinforcement learning. The encoder is a Bi-LSTM over the instruction. The decoder is an attentive LSTM over ResNet panoramic features.

**Stage 2.** The agent is fine-tuned on synthetic (environment, path, instruction) triplets produced by back-translation with a speaker model.

The authors' analysis shows that the real bottleneck of back-translation is the small number of distinct training environments, not the amount of data. To address this, they introduce "environmental dropout." It masks visual feature channels consistently across views and viewpoints, so that each mask behaves like a new house. The same mask is shared by the speaker and the follower.

The resulting agent took first place on the private test set of the VLN R2R leaderboard in every evaluation setting. This shows that cheaply simulating environment diversity can substantially narrow the gap between seen and unseen environments.

## Key points
- Back-translation with environmental dropout raises the best supervised (IL+RL) model by 5.7%, roughly three times the gain of plain back-translation. Feature-level dropout performs 3.8% worse and barely improves on the baseline.
- Giving the speaker and the follower separately sampled masks noticeably degrades results, so the shared mask is essential.
- Data-scaling experiments over the 60 training environments suggest a ceiling of about 52% success rate, even if all 190K routes in those environments were human-annotated. Adding new environments helps more than adding data.
- In the pre-exploration setting on validation-unseen environments, back-translation reaches 61.9% success rate, compared with 46.5% when using only training environments. Adding environmental dropout lifts this to 64.5%.
- With Matterport3D semantic views as input, results rise from 49.5% (IL+RL) to 52.0% with environmental dropout. Code is released at github.com/airsplay/R2R-EnvDrop.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of North Carolina at Chapel Hill](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-chapel-hill/) — University, Chapel Hill, United States · 37 works · 14,332 citations

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## Cite

Cite as: Hao Tan, Licheng Yu and Mohit Bansal. 2019. Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tan2019learning,
  title = {{Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout}},
  author = {Hao Tan, Licheng Yu and Mohit Bansal},
  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
  year = {2019},
  doi = {10.18653/v1/n19-1268},
  url = {https://doi.org/10.18653/v1/n19-1268}
}
```

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*Updated 24 October 2026*