Language understanding is essential for the navigation agent to follow
instructions. We observe two kinds of issues in the instructions that can make
the navigation task challenging: 1. The mentioned landmarks are not
recognizable by the navigation agent due to the different vision abilities of
the instructor and the modeled agent. 2. The mentioned landmarks are applicable
to multiple targets, thus not distinctive for selecting the target among the
candidate viewpoints. To deal with these issues, we design a translator module
for the navigation agent to convert the original instructions into
easy-to-follow sub-instruction representations at each step. The translator
needs to focus on the recognizable and distinctive landmarks based on the
agent's visual abilities and the observed visual environment. To achieve this
goal, we create a new synthetic sub-instruction dataset and design specific
tasks to train the translator and the navigation agent. We evaluate our
approach on Room2Room~(R2R), Room4room~(R4R), and Room2Room Last (R2R-Last)
datasets and achieve state-of-the-art results on multiple benchmarks.

通过设计一个翻译模块，将原有语句转化为易于理解的子语句，该模块针对航行代理的视觉能力和环境观察结果，聚焦于可识别和有区分性的地标，通过训练翻译器和导航代理的特定任务，达到了在多个基准测试中取得最新颖成果的目标。

VLN-Trans: 视觉语言导航代理翻译器

VLN-Trans: Translator for the Vision and Language Navigation Agent

Due to the unfamiliarity to particular words(or proper nouns) for
ingredients, non-native English speakers can be extremely confused about the
ordering process in restaurants like Subway. Thus, We developed a dialogue
system, which supports Chinese(Mandarin)1 and English2 at the same time. In
other words, users can switch arbitrarily between Chinese(Mandarin) and English
as the conversation is being conducted. This system is specifically designed
for Subway ordering3. In BilDOS, we designed a Discriminator module to tell the
language is being used in inputted user utterance, a Translator module to
translate used language into English if it is not English, and a Dialogue
Manager module to detect the intention within inputted user utterances, handle
outlier inputs by throwing clarification requests, map detected Intention and
detailed Keyword4 into a particular intention class, locate the current
ordering process, continue to give queries to finish the order, conclude the
order details once the order is completed, activate the evaluation process when
the conversation is done.

为了帮助非英语母语者在 Subway 餐厅点餐时解决单词翻译问题，我们开发了一个中英文混合的对话系统，包括翻译模块和对话管理模块等，用于帮助用户完成点餐流程。