With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

The availability of massive data and computing power allowing for effective data driven
neural approaches is having a major impact on machine learning and information retrieval …

Use of test collections and evaluation measures to assess the effectiveness of information
retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 …

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

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …

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

Due to the advantages in the cost-efficiency and reproducibility, user simulation has become
a promising solution to the user-centric evaluation of information retrieval systems …

Online evaluation is one of the most common approaches to measure the effectiveness of an
information retrieval system. It involves fielding the information retrieval system to real users …

Purpose–Together, increasing globalization and the internet created fertile grounds for the
establishment of multilingual digital libraries. Providing cross‐lingual access to materials is …

Predicting the performance of a search engine for a given query is a fundamental and
challenging task in information retrieval. Accurate performance predictors can be used in …