1 citations · 3 across the 6 of their papers we have counts for
6 papers
EMBRACE: Evaluation and Modifications for Boosting RACE
Mariia Zyrianova, Dmytro Kalpakchi, Johan Boye
When training and evaluating machine reading comprehension models, it is very important to work with high-quality datasets that are also representative of real-world reading compre…
SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in Swedish
Dmytro Kalpakchi, Johan Boye
We present SweCTRL-Mini, a large Swedish language model that can be used for inference and fine-tuning on a single consumer-grade GPU. The model is based on the CTRL architecture b…
Automatically generating question-answer pairs for assessing basic reading comprehension in Swedish
Dmytro Kalpakchi, Johan Boye
This paper presents an evaluation of the quality of automatically generated reading comprehension questions from Swedish text, using the Quinductor method. This method is a light-w…
Minor changes make a difference: a case study on the consistency of UD-based dependency parsers
Dmytro Kalpakchi, Johan Boye
Many downstream applications are using dependency trees, and are thus relying on dependency parsers producing correct, or at least consistent, output. However, dependency parsers a…
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset
Dmytro Kalpakchi, Johan Boye
An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options…
Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies
Dmytro Kalpakchi, Johan Boye
We propose a multilingual data-driven method for generating reading comprehension questions using dependency trees. Our method provides a strong, mostly deterministic, and inexpens…