Teacher:In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? Solve this instance: 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.
Student:
We introduce a new task and dataset on cross-lingual vision-language navigation, and propose a general cross-lingual VLN framework for the task.