Recently pre-trained language representation models such as BERT have shown great
success when fine-tuned on downstream tasks including information retrieval (IR). However …

Information retrieval systems traditionally rely on textual keywords to index and retrieve
documents. Keyword-based retrieval may return inaccurate and incomplete results when …

Pre-training and fine-tuning have achieved remarkable success in many downstream
natural language processing (NLP) tasks. Recently, pre-training methods tailored for …

Recently, the focus of many novel search applications shifted from short keyword queries to
verbose natural language queries. Examples include question answering systems and …

Searching is inherently an interactive process usually requiring numerous iterations of
querying and assessing in order to find the desired amount of relevant information …

Searching for relevant material that satisfies the information need of a user, within a large
document collection is a critical activity for web search engines. Query Expansion …

Search engines, by ranking a few links ahead of million others based on opaque rules, open
themselves up to criticism of bias. Previous research has focused on measuring political …

Several properties of information retrieval (IR) data, such as query frequency or document
length, are widely considered to be approximately distributed as a power law. This common …

Academic search concerns the retrieval and profiling of information objects in the domain of
academic research. In this paper we reveal important observations of academic search …

Modern Information Retrieval (IR) systems have become more and more complex, involving
a large number of parameters. For example, a system may choose from a set of possible …