论文标题
签证：视觉场景吸引机器翻译的模棱两可的字幕数据集
VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine Translation
论文作者
论文摘要
现有的多模式翻译（MMT）数据集由图像和视频字幕或一般字幕组成，这些字幕很少包含语言歧义，从而使视觉信息不太有效，无法生成适当的翻译。我们介绍Visa，这是一个由40k日语 - 英语平行句子对组成的新数据集和具有以下关键特征的相应视频剪辑：（1）并行句子是电影和电视剧集的字幕； （2）源字幕是模棱两可的，这意味着它们具有多种可能的翻译，其含义不同； （3）我们根据歧义的原因将数据集分为多义和省略。我们表明，Visa对于最新的MMT系统具有挑战性，我们希望数据集可以促进MMT研究。签证数据集可在以下网址获得：https：//github.com/ku-nlp/visa。
Existing multimodal machine translation (MMT) datasets consist of images and video captions or general subtitles, which rarely contain linguistic ambiguity, making visual information not so effective to generate appropriate translations. We introduce VISA, a new dataset that consists of 40k Japanese-English parallel sentence pairs and corresponding video clips with the following key features: (1) the parallel sentences are subtitles from movies and TV episodes; (2) the source subtitles are ambiguous, which means they have multiple possible translations with different meanings; (3) we divide the dataset into Polysemy and Omission according to the cause of ambiguity. We show that VISA is challenging for the latest MMT system, and we hope that the dataset can facilitate MMT research. The VISA dataset is available at: https://github.com/ku-nlp/VISA.
