Data-to-text (D2T) and text-to-data (T2D) are dual tasks that convert structured data, such as graphs or tables into fluent text, and vice versa. These tasks are usually handled separately and use corpora extracted from a single source. Current systems leverage pre-trained language models fine-tuned on D2T or T2D tasks. This approach has two main limitations: first, a separate system has to be tuned for each task and source; second, learning is limited by the scarcity of available corpora. This paper considers a more general scenario where data are available from multiple heterogeneous sources. Each source, with its specific data format and semantic domain, provides a non-parallel corpus of text and structured data. We introduce a variational auto-encoder model with disentangled style and content variables that allows us to represent the diversity that stems from multiple sources of text and data. Our model is designed to handle the tasks of D2T and T2D jointly. We evaluate our model on several datasets, and show that by learning from multiple sources, our model closes the performance gap with its supervised single-source counterpart and outperforms it in some cases.

翻译：数据到文本（D2T）与文本到数据（T2D）是双向互补任务，前者将结构化数据（如图表或表格）转化为流畅文本，后者则反之。这两类任务通常被分开处理，且使用的语料库往往来源于单一数据源。当前系统依赖在D2T或T2D任务上微调的预训练语言模型，但该方法存在两大局限：其一，需为每个任务和数据源分别调整独立系统；其二，学习过程受限于可用语料匮乏。本文考虑更普适的场景——数据来自多个异构源。每个数据源以其独特的数据格式和语义领域，提供非平行文本与结构化数据语料。我们提出一种解耦风格与内容变量的变分自编码器模型，能够表征多文本与多数据源产生的多样性。该模型被设计为同时处理D2T与T2D任务。通过在多数据集上的评估表明，多源学习使模型性能与监督式单源基线模型的差距得以消除，甚至在某些场景下超越后者。