论文标题
使用预验证的序列模型进行文档排名
Document Ranking with a Pretrained Sequence-to-Sequence Model
论文作者
论文摘要
这项工作提出了一种对验证序列到序列模型对文档排名任务的新颖调整。我们的方法与基于仅经过验证的变压器体系结构（例如BERT）的基于通常基于分类的排名的表述从根本上有所不同。我们展示了如何训练序列到序列模型以生成相关标签为“目标词”，以及如何将这些目标词的基础逻辑解释为排名的相关概率。在流行的MS MARCO段落排名任务上，实验结果表明，我们的方法至少与以前的基于分类的模型相当，并且可以使用更大，更具焦点的模型超越它们。在TREC 2004鲁棒轨道的测试收集中，我们演示了一种基于零传输的方法，该方法的表现优于先前需要的最新模型，需要内部跨验证。此外，我们发现我们的方法在数据贫乏的制度中显着优于仅编码模型（即很少有培训示例）。我们通过改变目标词来进一步研究这一观察结果，以探究模型对潜在知识的使用。
This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge.
