LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach
Not on the map. Why?
Abstract (arXiv)
A LightGBM model fed with target word lexical characteristics and features obtained from word frequency lists, psychometric data and bigram association measures has been optimized for the 2021 CMCL Shared Task on Eye-Tracking Data Prediction. It obtained the best performance of all teams on two of the five eye-tracking measures to predict, allowing it to rank first on the official challenge criterion and to outperform all deep-learning based systems participating in the challenge.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 35 references that are in the map. First posted on arXiv 27 Apr 2021.
Builds on
2 of 35 references.
References · 2
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 2.
5 papers.
- 2021Reading Task Classification Using EEG and Eye-Tracking Datacited by 5
- 2023Probing Large Language Models from A Human Behavioral Perspectivecited by 5
- 2022A simple language-agnostic yet very strong baseline system for hate speech and offensive content identificationcited by 2
- 2026Measuring Alignment With Reader Highlights Net of Position and Lengthcited by 1
- 2026Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Readingcited by 0
- 2026Measuring Alignment With Reader Highlights Net of Position and Length30 Jul 2026
- 2026Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading1 Jun 2026
- 2023Probing Large Language Models from A Human Behavioral Perspective8 Oct 2023
- 2022A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification5 Feb 2022
- 2021Reading Task Classification Using EEG and Eye-Tracking Data12 Dec 2021