神经寻回与超越:论文提案

Man Luo
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引用次数: 1

摘要

信息检索器(Information retrieval, IR)旨在大规模地查找与给定查询相关的文档(例如片段、段落和文章)。IR在许多需要外部知识的任务中发挥着重要作用,例如开放领域问答和对话系统。过去,基于词匹配的搜索算法得到了广泛的应用。近年来,基于神经的算法(称为神经检索器)得到了越来越多的关注,它可以减轻传统方法的局限性。尽管神经检索器取得了成功,但它们仍然面临许多挑战,例如训练数据量少,无法回答简单的以实体为中心的问题。此外,现有的大多数神经检索器都是针对纯文本查询开发的。这阻止了它们处理多模态查询(即查询由文本描述和图像组成)。这项提议有两个目标。首先,我们从新的模型架构、面向ir的预训练任务和生成大规模训练数据三个角度介绍了解决神经检索器上述问题的方法。其次,我们确定了未来的研究方向,并提出了可能的相应解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Retriever and Go Beyond: A Thesis Proposal
Information Retriever (IR) aims to find the relevant documents (e.g. snippets, passages, and articles) to a given query at large scale. IR plays an important role in many tasks such as open domain question answering and dialogue systems, where external knowledge is needed. In the past, searching algorithms based on term matching have been widely used. Recently, neural-based algorithms (termed as neural retrievers) have gained more attention which can mitigate the limitations of traditional methods. Regardless of the success achieved by neural retrievers, they still face many challenges, e.g. suffering from a small amount of training data and failing to answer simple entity-centric questions. Furthermore, most of the existing neural retrievers are developed for pure-text query. This prevents them from handling multi-modality queries (i.e. the query is composed of textual description and images). This proposal has two goals. First, we introduce methods to address the abovementioned issues of neural retrievers from three angles, new model architectures, IR-oriented pretraining tasks, and generating large scale training data. Second, we identify the future research direction and propose potential corresponding solution.
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