Question Difficulty Estimation from Text (QDET) is the application of Natural Language
Processing techniques to the estimation of a value, either numerical or categorical, which …

This work presents a comparative analysis of various machine learning (ML) methods for
predicting item difficulty in English reading comprehension tests using text features extracted …

We present a parametrizable approach to exercise generation from authentic texts that
addresses the need for digital materials designed to practice the language means on the …

Annotation studies often require annotators to familiarize themselves with the task, its
annotation scheme, and the data domain. This can be overwhelming in the beginning …

Abstract Question Difficulty Estimation from Text (QDET) received an increased research
interest in recent years, but most of previous work focused on single silos, without …

This paper presents the first multi-objective transformer model for constructing open cloze
tests that exploits generation and discrimination capabilities to improve performance. Our …

Difficulty-controllable question generation (DCQG) over knowledge graphs aims to generate
questions with a given subgraph and a difficulty label, such as “easy” or “hard.” However …

In this work, we report on a crowdsourcing experiment conducted using the V-TREL
vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge …

This paper presents a pilot study of entropy as a measure of gap complexity in open cloze
tests aimed at learners of English. Entropy is used to quantify the information content in each …

Existing approaches to active learning maximize the system performance by sampling
unlabeled instances for annotation that yield the most efficient training. However, when …