Papers with reading
Eye Tracking and NLP (2025.acl-tutorials)
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| Challenge: | tutorial combines eye tracking during reading with NLP . outlines how eye movements in reading can be leveraged for NLP methods . |
| Approach: | The tutorial combines eye tracking during reading with NLP . it covers eye movements in reading, integrating eye movement data in NLP models . |
| Outcome: | The tutorial outlines how eye movements in reading can be leveraged for NLP . it provides the essential background for conducting research on joint modeling of eye movements and text. |
Reasoning Over Paragraph Effects in Situations (D19-58)
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| Challenge: | a key component of reading a passage is the ability to apply knowledge gained from the passage to a new situation. |
| Approach: | They propose a benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. |
| Outcome: | The proposed model performs slightly better than randomly guessing an answer of the correct type, but is below the human performance of 89.0%. |
BOOM: Beyond Only One Modality KIT’s Multimodal Multilingual Lecture Companion (2026.eacl-demo)
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Sai Koneru, Fabian Retkowski, Christian Huber, Lukas Hilgert, Seymanur Akti, Enes Yavuz Ugan, Alexander Waibel, Jan Niehues
| Challenge: | a multimodal multilingual lecture companion is needed to preserve lecture content in its entirety . globalization of education and rapid growth of online learning have made localizing educational content a challenge . |
| Approach: | They propose a multimodal multilingual lecture companion that translates lecture audio and slides to produce synchronized outputs across three modalities. |
| Outcome: | The proposed solution preserves the original content in its entirety while preserving translations across three modalities. |
MadDog: A Web-based System for Acronym Identification and Disambiguation (2021.eacl-demos)
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| Challenge: | Acronyms and abbreviations are the short-form of longer phrases and are frequently used in writing but they can also present challenges for newcomers. |
| Approach: | They propose to develop a web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. |
| Outcome: | The proposed system can process acronyms from scientific, biomedical, and general domains. |
Authorship Identification for Literary Book Recommendations (C18-1)
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| Challenge: | Existing systems that predict whether a user likes a book are collaborative filtering and content-based recommendation. |
| Approach: | They propose a system that learns authors' style and uses it to recommend books . they also evaluated whether similar books/authors were annotated similarly by experts . |
| Outcome: | The proposed system gives better accuracy when compared with state-of-the-art methods. |
SW4ALL: a CEFR Classified and Aligned Corpus for Language Learning (L18-1)
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| Challenge: | Learning a second language requires exposition to texts, especially for the acquisition of vocabulary. |
| Approach: | They propose a corpus of documents classified by language proficiency level . they use alignments between the English Wikipedia and the Simple English Wikipedia . |
| Outcome: | The SW4ALL corpus contains 8,669 pairs of documents that present different levels of proficiency. |
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020.emnlp-main)
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| Challenge: | Current machine reading comprehension benchmarks have no questions that test temporal phenomena . a new study studies reading comprehension for temporal relations . |
| Approach: | They propose a reading comprehension benchmark built on news snippets and 21k human-generated questions querying temporal relationships. |
| Outcome: | The new reading comprehension benchmark TORQUE achieves an exact-match score of 51% on the test set . the benchmark is built on 3.2k news snippets with 21k human-generated questions . |
ScanEZ: Integrating Cognitive Models with Self-Supervised Learning for Spatiotemporal Scanpath Prediction (2025.acl-short)
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| Challenge: | ScanEZ framework provides a framework for predicting scanpaths during reading . masked modeling of eye movements and cognitive model simulations are used to kick-start training. |
| Approach: | They propose a framework for self-supervised learning that models scanpaths using synthetic data and a 3-D gaze objective inspired bymasked language modeling. |
| Outcome: | The proposed framework achieves state-of-the-art results on established datasets and is portable across different conditions. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
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| Challenge: | Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics. |
| Approach: | They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions. |
| Outcome: | The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results. |
Automatically Estimating Textual and Phonemic Complexity for Cued Speech: How to See the Sounds from French Texts (2024.lrec-main)
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| Challenge: | Cued Speech (CS) is a visual communication system developed for people with hearing loss to complement speech reading at the phonetic level. |
| Approach: | They propose a method to phonemize written corpora so that each word is aligned with the corresponding CS key(s) this method is part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user will be able to choose a text upon its complexity for cueing. |
| Outcome: | The proposed method is part of a wider project aimed at creating an augmented reality system displaying a virtual coding hand where the user can choose a text upon its complexity for cueing. |
Assessing Language Proficiency from Eye Movements in Reading (N18-1)
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| Challenge: | a novel approach to determine second language proficiency uses behavioral traces of eye movements during reading . over 1.5 billion people are learning English as a second language worldwide . traditional approaches to language proficiency testing have several drawbacks, including the fact that they are typically prepared manually and require extensive resources for test development . |
| Approach: | They propose a method which uses behavioral traces of eye movements during reading to determine learners’ second language proficiency. |
| Outcome: | The proposed approach correlates with standardized English proficiency tests and is validated by eyetracking with eye movements from other readers. |
Neural Language Taskonomy: Which NLP Tasks are the most Predictive of fMRI Brain Activity? (2022.naacl-main)
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| Challenge: | Existing literature has focused on pretrainer-based text-driven brain encoding models . however, few studies have explored the efficacy of task-specific learning of Transformers . |
| Approach: | They propose to use ten popular natural language processing tasks to learn Transformer representations for predicting brain responses. |
| Outcome: | The proposed model predicts brain activity across the whole brain. |
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. |
Empowering Active Learning to Jointly Optimize System and User Demands (2020.acl-main)
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| Challenge: | Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. |
| Approach: | They propose an active learning approach that integrates active learning with an end-user application to optimize the user's training and receiving useful instances. |
| Outcome: | The proposed approach satisfies both objectives when alternative methods lead to many unsuitable exercises for end users. |
Speech language models lack important brain-relevant semantics (2024.acl-long)
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| Challenge: | Recent work shows that text-based language models predict both text- and speech-evoked brain activity. |
| Approach: | They remove low-level stimulus features from language models to assess their impact on alignment with fMRI brain recordings during reading and listening. |
| Outcome: | The proposed model removes low-level features from fMRI brain recordings to assess their impact on alignment with fmr recordings. |
Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences (2024.findings-acl)
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| Challenge: | incorporating cognitive capacities increases predictive power of surprisal and entropy measures on reading data, whereas high performance in the psychometric tests is associated with lower sensitivity to predictability effects. |
| Approach: | They examine the predictive power (PP) of surprisal and entropy estimated from generative language models (LMs) on reading data from individuals who also completed a wide range of psychometric tests. |
| Outcome: | The LMs' predictive power is based on cognitive capacities and high performance in psychometric tests is associated with lower sensitivity to predictability effects. |
Automating Easy Read Text Segmentation (2024.findings-emnlp)
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| Challenge: | Existing methods for automatic segmentation of Easy Read text have not been explored in detail. |
| Approach: | They propose automated methods for Easy Read segmentation that leverage masked and generative language models and constituent parsing to evaluate their viability. |
| Outcome: | The proposed methods are compared with human-driven segmentation in three languages. |
Context Limitations Make Neural Language Models More Human-Like (2022.emnlp-main)
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| Challenge: | Language models (LMs) have been used in cognitive modeling and engineering studies to simulate human cognitive load during reading. |
| Approach: | They propose to constrain LMs' context access to improve their simulation of human reading behavior by incorporating syntactic biases into their context access. |
| Outcome: | The proposed model improves the simulation of human reading behavior by incorporating syntactic biases into their context access. |
InteRead: An Eye Tracking Dataset of Interrupted Reading (2024.lrec-main)
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| Challenge: | Eye movements during reading can provide insights into cognitive processes and language comprehension, but the scarcity of reading data with interruptions hampers advances in the development of intelligent learning technologies. |
| Approach: | They propose a dataset of eye movements during reading that includes eye movements and word frequency effects. |
| Outcome: | The proposed dataset shows that interruptions, word length and word frequency effects significantly impact eye movements during reading. |
Reading Does Not Equal Reading: Comparing, Simulating and Exploiting Reading Behavior across Populations (2024.lrec-main)
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| Challenge: | Existing corpora of eye-tracking-while-reading corporata lack diversity, limiting their ability to include primarily native speakers. |
| Approach: | They expand the eye-tracking-while-reading dataset CopCo by incorporating a new dataset of L2 readers with diverse L1 backgrounds. |
| Outcome: | The extended CopCo corpus comprises neurotypical L1 and L1 readers with dyslexia as well as L2 readers reading the same materials. |
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior (2025.acl-long)
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| Challenge: | Standard models that focus on fixation durations ignore spatial dynamics of reading . authors propose a model that captures how long fixations last, where they land and when . |
| Approach: | They propose a generative model that captures how long fixations last and where they land and when they occur. |
| Outcome: | The proposed model exhibits higher likelihood on held-out reading data than baselines. |