论文标题
超越[CLS]通过一代排名
Beyond [CLS] through Ranking by Generation
论文作者
论文摘要
信息检索的生成模型，其中文档的排名被视为从文档的语言模型中生成查询的任务，在过去的各种IR任务中都非常成功。但是，随着现代深度神经网络的出现，注意力转向了歧视性排名函数，这些函数对文档和查询的语义相似性进行了建模。最近，诸如GPT2和BART之类的深层生成模型已被证明是出色的文本生成器，但尚未证明它们作为排名的有效性。在这项工作中，我们重新审视了信息检索的生成框架，并表明我们的生成方法与基于语义相似性的最先进的歧视模型一样有效。此外，我们证明了IR不可能损失的有效性。
Generative models for Information Retrieval, where ranking of documents is viewed as the task of generating a query from a document's language model, were very successful in various IR tasks in the past. However, with the advent of modern deep neural networks, attention has shifted to discriminative ranking functions that model the semantic similarity of documents and queries instead. Recently, deep generative models such as GPT2 and BART have been shown to be excellent text generators, but their effectiveness as rankers have not been demonstrated yet. In this work, we revisit the generative framework for information retrieval and show that our generative approaches are as effective as state-of-the-art semantic similarity-based discriminative models for the answer selection task. Additionally, we demonstrate the effectiveness of unlikelihood losses for IR.
