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.

本篇论文提出三个方面的方法，包括新的模型架构、IR 导向的预训练任务、生成大规模训练数据，以解决神经检索器面临的问题，并探讨了多模式查询的未来研究方向和相应的解决方法。