You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction (2020.acl-main)
Copied to clipboard
Orion Weller, Jordan Hildebrandt, Ilya Reznik, Christopher Challis, E. Shannon Tass, Quinn Snell, Kevin Seppi
| Challenge: | Existing work on reading time prediction has focused on word level only predictions . however, previous work has focused only on word levels . |
| Approach: | They perform an experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. |
| Outcome: | The proposed method combines a large number of machine learning methods with textual and stylistic factors to predict the time it takes to read. |
Similar Papers
On the Role of Context in Reading Time Prediction (2024.emnlp-main)
Copied to clipboard
| Challenge: | a new perspective on how readers integrate context during reading time prediction is presented . a recent study shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
| Approach: | They propose a technique where they project surprisal onto the orthogonal complement of frequency. |
| Outcome: | The proposed method shows that the proportion of variance in reading times explained by context is smaller when context is represented by the orthogonalized predictor. |
A Neural Model of Adaptation in Reading (D18-1)
Copied to clipboard
| Challenge: | Several studies suggest that readers do adapt their lexical and syntactic predictions to the current context. |
| Approach: | They propose to add a simple adaptation mechanism to a neural language model to improve predictions of reading times. |
| Outcome: | The proposed model improves predictions of human reading times compared to a non-adaptive model. |
Beyond the Average Reader: the Reader Embedding Approach (2025.findings-acl)
Copied to clipboard
| Challenge: | a new approach to predict reading times is proposed to use eye-tracking data to collect data from all subjects rather than from the most similar ones. |
| Approach: | They propose a method to collect eye-tracking data that are averaged and used to train learning models. |
| Outcome: | The proposed approach outperforms existing methods by combining eye-tracking data with averaged data. |
Fine-Grained Prediction of Reading Comprehension from Eye Movements (2024.emnlp-main)
Copied to clipboard
| Challenge: | a new study attempts to assess reading comprehension from eye movements in reading . eye movements provide small improvements over a text-only baseline, the authors argue . |
| Approach: | They propose to use eyetracking data to predict reading comprehension of a single participant . they use a battery of recent models and three new multimodal language models . |
| Outcome: | The proposed model can predict reading comprehension of a single participant from eye movements over a paragraph. |
Reading Time and Vocabulary Rating in the Japanese Language: Large-Scale Japanese Reading Time Data Collection Using Crowdsourcing (2022.lrec-1)
Copied to clipboard
| Challenge: | a study examines how differences in human vocabulary affect reading time . vocabulary size is inversely correlated to reading time due to the COVID-19 pandemic . |
| Approach: | They assume that vocabulary is random effect of research participants . they then asked participants to take part in a self-paced reading task to collect reading times . |
| Outcome: | The proposed method clarifies the tendency that vocabulary differences give to reading time. |
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
Copied to clipboard
| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
| Outcome: | The proposed models perform on 14 out of 20 bAbI, SQuAD, CBT, CNN and Who-did-What datasets. |
Exploring the Effect of Nominal Compound Structure in Scientific Texts on Reading Times of Experts and Novices (2025.acl-srw)
Copied to clipboard
| Challenge: | Using a corpus of eye-tracking data of German native speakers, we find that some compound types are associated with longer reading times. |
| Approach: | They use a corpus containing eye-tracking data of german native speakers reading scientific texts. |
| Outcome: | The authors show that some compound types are associated with longer reading times and that experts may have an advantage while reading in-domain texts, but also while reading out-of-domain. |
Temperature-scaling surprisal estimates improve fit to human reading times – but does it do so for the “right reasons”? (2024.acl-long)
Copied to clipboard
| Challenge: | a wide body of evidence shows that human language processing difficulty is predicted by the information-theoretic measure surprisal, a word’s negative log probability in context. |
| Approach: | They propose to use large language models to predict the surprisal of a word's negative log probability in context to test their predictive power. |
| Outcome: | The proposed model can be significantly more accurate than humans because it has more data. |
Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension (2025.coling-main)
Copied to clipboard
| Challenge: | Several studies have focused on predicting the surprisal of a word and its reading time, but only recently, attention has been given to other components, such as P600. |
| Approach: | They propose to model reading times and ERP amplitudes using surprisal and entropy . they also propose a metric based on semantic similarity for N400 and P600 . |
| Outcome: | The proposed metric predicts reading times and ERP amplitudes in Mandarin Chinese. |
Towards Understanding Text Factors in Oral Reading (N18-1)
Copied to clipboard
| Challenge: | Using a case study, we show that variation in oral reading rate is consistent across readers. |
| Approach: | They propose to use text complexity to predict reading rate for professional narrators . they also show that variation can be explained by timing and story-based factors . |
| Outcome: | The authors show that variation in reading rate can be explained by features of the texts being read. |