# LAST at CMCL 2021 Shared Task: Predicting Gaze Data During Reading with a Gradient Boosting Decision Tree Approach

arXiv: https://arxiv.org/abs/2104.13043 · HTML: https://mlsanity.com/paper/2104.13043/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [CogniVal: A Framework for Cognitive Word Embedding Evaluation](https://mlsanity.com/paper/1909.09001/) (2019)

## Built on by (most cited first, 5 of 5)

- [Reading Task Classification Using EEG and Eye-Tracking Data](https://mlsanity.com/paper/2112.06310/) (2021)
- [Probing Large Language Models from A Human Behavioral Perspective](https://mlsanity.com/paper/2310.05216/) (2023)
- [A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification](https://mlsanity.com/paper/2202.02511/) (2022)
- [Measuring Alignment With Reader Highlights Net of Position and Length](https://mlsanity.com/paper/2607.27739/) (2026)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)

## 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.