Recently, much progress in natural language processing has been driven by deep contextualized representations pretrained on large corpora. Typically, the fine-tuning on these pretrained models for a specific downstream task is based on single-view learning, which is however inadequate as a sentence can be interpreted differently from different perspectives. Therefore, in this work, we propose a text-to-text multi-view learning framework by incorporating an additional view -- the text generation view -- into a typical single-view passage ranking model. Empirically, the proposed approach is of help to the ranking performance compared to its single-view counterpart. Ablation studies are also reported in the paper.

中文翻译：

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文本到文本多视图学习，以对段落进行重新排序

最近，在大型语料库上预先训练的深度上下文表示法已经推动了自然语言处理方面的许多进步。通常，针对特定下游任务的这些预训练模型的微调是基于单视图学习的，但是这是不足的，因为可以从不同的角度对句子进行不同的解释。因此，在这项工作中，我们通过将一个额外的视图（文本生成视图）合并到典型的单视图段落排名模型中，提出了一个文本到文本的多视图学习框架。从经验上讲，与单视图方法相比，该方法有助于排名性能。该论文还报道了消融研究。