This work presents a joint solution to two challenging tasks: text generation from data and open information extraction. We propose to model both tasks as sequence-to-sequence translation problems and thus construct a joint neural model for both. Our experiments on knowledge graphs from Visual Genome, i.e., structured image analyses, shows promising results compared to strong baselines. Building on recent work on unsupervised machine translation, we report the first results - to the best of our knowledge - on fully unsupervised text generation from structured data.
翻译:这项工作为两项具有挑战性的任务提供了共同的解决办法:从数据中生成文本和公开的信息提取。我们提议将这两项任务作为顺序到顺序的翻译问题来模拟,从而为两者建立一个联合神经模型。我们对视觉基因组知识图表的实验,即结构化图像分析,显示了与强力基线相比的有希望的结果。根据最近进行的未经监督的机器翻译工作,我们根据我们的知识,报告了从结构化数据中完全不受监督的文本生成的第一批结果。