user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cross-Lingual Vision-Language Navigation

Paper content:
Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic unknown environments with natural language instructions.
Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions.
The ultimate goal of VLN, however, is to serve people speaking arbitrary languages.
Towards multilingual VLN with numerous languages, we collect a cross-lingual R2R dataset, which extends the original benchmark with corresponding Chinese instructions.
But it is time-consuming and expensive to collect large-scale human instructions for every existing language.
Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages.
We first explore the possibility of building a cross-lingual agent when no training data of the target language is available.
The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and a visually grounded cross-lingual alignment module to align textual representations of different languages.
Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions.
In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data.
Our methods and dataset demonstrate the potentials of building a cross-lingual agent to serve speakers with different languages.
Recently, the Vision-Language Navigation (VLN) task (Anderson et al., 2018) , which requires the agent to follow natural language instructions and navigate in houses, has drawn significant attention.
In contrast to some existing navigation tasks (Mirowski et al., 2016; Zhu et al., 2017) , where the agent has an explicit representation of the target to know if the goal has been reached or not, an agent in the VLN task can only infer the target from natural language instructions.
Therefore, in addition to normal visual challenges in navigation tasks, language understanding and cross-modal alignment are essential to complete the VLN task.
However, existing benchmarks (Anderson et al., 2018; Chen et al., 2019) for the VLN task are all monolingual in that they only contain English instructions.
Specifically, the navigation agents are trained and tested with only English corpus and thus unable to serve non-English speakers.
To fill this gap, one can collect the corresponding instructions in the language that the agent is expected to execute.
But it is not scalable and practical as there are thousands of languages on this planet and collecting large-scale data for each language would be very expensive and time-consuming.
Therefore, in this paper, we study the task of cross-lingual VLN to endow an agent the ability to understand multiple languages.
First, can we learn a model that has been trained on existing English instructions but is still able to perform reasonably well on a different language (e.g. Chinese)?
This is indeed a zero-shot learning scenario where no training data of target language is available.
An intuitive approach is to train the agent with English data, and at test time, use a machine translation system to translate the target language instructions to English, which are then fed into the agent for testing (see the above part of Figure 1 ).
The inverse solution is also rational: we can translate all English instructions into the target language and train the agent on the translated data, so it can be directly tested with target language instructions (see the below part of Figure 1) .
The former agent is tested on translated instructions while the latter is trained on translated instructions.
Both solutions suffer from translation errors and deviation from the corresponding human-annotated instructions.
But meanwhile, the former is trained on human-annotated English instructions (which we view as "golden" data) and the latter is tested on "golden" target language instructions.
Motivated by this fact, we design a cross-lingual VLN framework that learns to benefit from both solutions.
As shown in Figure 1 , we combine these two principles and introduce a meta-learner, which learns to produce beliefs for human-annotated instruction and its translation pair and dynamically fuse the cross-lingual representations for better navigation.
In this case, however, the training and inference are mismatched.
During training, the agent takes source human language and target machine translation (MT) data as input, while during inference, it needs to navigate with target human instructions and source MT data.
To better align the source and target languages, we propose a visually grounded cross-lingual alignment module to align the paired instructions via the same visual feature because they are describing the same demonstration path.
The cross-lingual alignment loss can also implicitly alleviate the translation errors by aligning the human language and its MT pair in the latent visual space.
After obtaining an efficient zero-shot agent, we investigate the question that, given a certain amount of data for the target language, can we learn a better adaptation model to improve source-to-target knowledge transfer?
The meta-learner and visually grounded cross-lingual alignment module provide a foundation for solving the circumstances that the agent has access to the source language and (partial) target language instructions for training.
To further leverage the fact that the agent has access to the target language training data, we introduce an adversarial domain adaption loss to alleviate the domain shifting issue between human-annotated and MT data, thus enhancing the model's transferring ability.
To validate our methods, we collect a cross-lingual VLN dataset (XL-R2R) by extending complimentary Chinese instructions for the English instructions in the R2R dataset.
Overall, our contributions are four-fold: (1) We collect the first cross-lingual VLN dataset to facilitate navigation models towards accomplishing instructions of various languages such as English and Chinese, and conduct analysis between English and Chinese corpus.
(2) We introduce the task of cross-lingual visionlanguage navigation and propose a principled meta-learning method that dynamically utilizes the augmented MT data for zero-shot cross-lingual VLN.
(3) We propose a visually grounded crosslingual alignment module for better cross-lingual knowledge transfer.
(4) We investigate how to transfer knowledge between human-annotated and MT data and introduce an adversarial domain adaption loss to improve the navigation performance given a certain amount of human-annotated target language data.
In this paper, we introduce a new task, namely cross-lingual vision-language navigation, to study cross-lingual representation learning situated in the navigation task where cross-modal interaction with the real world is involved.
We collect a cross-lingual R2R dataset and conduct pivot studies towards solving this challenging but practical task.
The proposed cross-lingual VLN framework is proven effective in cross-lingual knowledge transfer.
There are still lots of promising future directions for this task and dataset, e.g. to incorporate recent advances in VLN and greatly improve the model capacity.
It would also be valuable to extend the cross-lingual setting to support numerous different languages in addition to English and Chinese.

assistant: text: We introduce a new task and dataset on cross-lingual vision-language navigation, and propose a general cross-lingual VLN framework for the task.