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

Bestgen, Yves

doi:10.18653/v1/2021.cmcl-1.10

Cited by 5 publications

(4 citation statements)

References 21 publications

(16 reference statements)

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“…• A much slower and more complex approach to optimize because it requires the optimization of many parameters, but that has recently outperformed all deep-learning based systems participating in the CMCL 2021 shared task on predicting gaze data during reading (Bestgen, 2021a): a gradient boosting decision tree approach as implemented in the LightGBM free software (Ke et al, 2017). This approach has been used only in a second time.…”

Section: Proposed Systemmentioning

confidence: 99%

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

Bestgen¹

2022

Preprint

Self Cite

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For automatically identifying hate speech and offensive content in tweets, a system based on a classical supervised algorithm only fed with character n-grams, and thus completely language-agnostic, is proposed by the SATLab team. After its optimization in terms of the feature weighting and the classifier parameters, it reached, in the multilingual HASOC 2021 challenge, a medium performance level in English, the language for which it is easy to develop deep learning approaches relying on many external linguistic resources, but a far better level for the two less resourced language, Hindi and Marathi. It ends even first when performances are averaged over the three tasks in these languages, outperforming many deep learning approaches. These performances suggest that it is an interesting reference level to evaluate the benefits of using more complex approaches such as deep learning or taking into account complementary resources.

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Section: Proposed Systemmentioning

confidence: 99%

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

Bestgen¹

2022

Preprint

Self Cite

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“…More specifically, these authors train models such as logistic regression or conditional random fields on a corpus of human eye-tracking data, and then predict fixation time, skipping rate, and other eye-movement measures on an unseen test set. Unlike our goals here, the literature in this tradition (e.g., Bestgen, 2021;Hara, Kano, & Aizawa, 2012;Hollenstein et al, 2021;Matthies & Søgaard, 2013;Nilsson & Nivre, 2009 does not primarily aim to construct explanatory cognitive models, and the use of supervised training (i.e., models learn their behavior from a pre-existing training set of eye-movement data) is not psychologically realistic, as outlined in the introduction. However, machine learning-based models typically achieve good prediction accuracy, which makes them suitable for comparison with cognitive models.…”

Section: Models Of Eye-movement Controlmentioning

confidence: 99%

Modeling Task Effects in Human Reading with Neural Network-based Attention

Hahn¹,

Keller²

2022

Preprint

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Research on human reading has long documented that reading behavior shows task-specific effects, but it has been challenging to build general models predicting what reading behavior humans will show in a given task. We introduce NEAT, a computational model of the allocation of attention in human reading, based on the hypothesis that human reading optimizes a tradeoff between economy of attention and success at a task. Our model is implemented using contemporary neural network modeling techniques, and makes explicit and testable predictions about how the allocation of attention varies across different tasks. We test this in an eyetracking study comparing two versions of a reading comprehension task, finding that our model successfully accounts for reading behavior across the tasks. Our work thus provides evidence that task effects can be modeled as optimal adaptation to task demands.

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“…For the present benchmark, this is possible by leveraging a naturalistic dataset of reading English sentences, the Zurich Cognitive Language Processing Corpus (Hollenstein et al, 2018 , 2020 ). The ZuCo dataset is publicly available and has recently been used in a variety of applications including leveraging EEG and eye-tracking data to improve NLP tasks (Barrett et al, 2018 ; Mathias et al, 2020 ; McGuire and Tomuro, 2021 ), evaluating the cognitive plausibility of computational language models (Hollenstein et al, 2019b ; Hollenstein and Beinborn, 2021 ), investigating the neural dynamics of reading (Pfeiffer et al, 2020 ), developing models of human reading (Bautista and Naval, 2020 ; Bestgen, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

Hollenstein

Tröndle²,

Płomecka³

et al. 2023

Front. Psychol.

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We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading of English sentences. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com.

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

Cited by 5 publications

References 21 publications

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

A simple language-agnostic yet very strong baseline system for hate speech and offensive content identification

Modeling Task Effects in Human Reading with Neural Network-based Attention

The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

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