| 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. |
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You Don’t Have Time to Read This: An Exploration of Document Reading Time Prediction (2020.acl-main)
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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. |
The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writing (2025.findings-acl)
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| Challenge: | Recent studies comparing AI-generated and human-authored literary texts have produced conflicting results. |
| Approach: | They hypothesize that differences in reading quality can be explained by genuine differences in how readers interpret and value literature . |
| Outcome: | The authors show that the differences in reading quality are largely explained by differences in how readers interpret and value literature, rather than by an intrinsic quality of the texts evaluated. |
Word Complexity is in the Eye of the Beholder (2021.naacl-main)
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| Challenge: | Lexical complexity is a subjective notion, yet it is often neglected in lexical simplification and readability systems which use a ”one-size-fits-all” approach. |
| Approach: | They propose to use a dataset of complex words annotated by readers with different backgrounds to investigate which aspects contribute to the notion of lexical complexity. |
| Outcome: | The proposed approach can be replicated in a dataset of complex words annotated by readers with different backgrounds. |
Exploring the Effect of Nominal Compound Structure in Scientific Texts on Reading Times of Experts and Novices (2025.acl-srw)
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| 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. |
Familiar words but strange voices: Modelling the influence of speech variability on word recognition (2021.eacl-srw)
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| Challenge: | Despite the lack of acoustic-phonetic invariance in speech, listeners can reliably recognize spoken words despite the lack aural-phonemic invariancy. |
| Approach: | They propose a deep neural model which is trained to retrieve the meaning of a word given its spoken form, a task which resembles that faced by a human listener. |
| Outcome: | The proposed model is more sensitive to dialectical variation than gender variation and more related to related languages. |
What Makes Reading Comprehension Questions Difficult? (2022.acl-long)
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| Challenge: | a recent study shows that natural language understanding benchmarks are not able to measure future progress . a crowdsourcing approach is needed to collect diverse examples without sacrificing diversity or coverage. |
| Approach: | They crowdsource multiple-choice reading comprehension questions for passages from seven sources . they find passage source, length, and readability measures do not significantly affect question difficulty . |
| Outcome: | The results show that passage source, length, and readability measures do not significantly affect question difficulty. |
The Natural Stories Corpus (L18-1)
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Richard Futrell, Edward Gibson, Harry J. Tily, Idan Blank, Anastasia Vishnevetsky, Steven Piantadosi, Evelina Fedorenko
| Challenge: | Existing corpora of naturalistic text do not contain the low-frequency syntactic constructions needed to distinguish between theories. |
| Approach: | They propose to compare models of language processing by comparing their ability to predict behavioral and neural measures of processing difficulty to corpora of naturalistic text. |
| Outcome: | The proposed corpus contains low-frequency syntactic constructions while sounding fluent to native speakers. |
On the Role of Context in Reading Time Prediction (2024.emnlp-main)
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| 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. |
Do Language Models Exhibit Human-like Structural Priming Effects? (2024.findings-acl)
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| Challenge: | a recent exposure to a structure facilitates processing of the same structure, a study finds . structural priming is well attested in humans, for both language production and comprehension . |
| Approach: | They use the structural priming paradigm to investigate where priming effects manifest . they find that rarer elements within a prime increase priming effect . |
| Outcome: | The findings provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models. |
Genre Matters: How Text Types Interact with Decoding Strategies and Lexical Predictors in Shaping Reading Behavior (2025.emnlp-main)
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| Challenge: | eMTeC is the first eye-tracking corpus of LLM-generated texts . it shows that text type strongly modulates cognitive effort during reading . |
| Approach: | They use the first eye-tracking corpus of LLM-generated texts to study eye movements during reading and how decoding strategies interact with text types to shape reading behavior. |
| Outcome: | The first eye-tracking corpus of LLM-generated texts shows that text type strongly modulates cognitive effort during reading and that word-level psycholinguistic effects vary systematically across genres. |