ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts (2023.emnlp-main)
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| Challenge: | Eye movements in reading are a key part of psycholinguistic research, but the lack of eye movement data and its unavailability at application time pose a major challenge for this line of research. |
| Approach: | They propose a novel sequence-to-sequence diffusion model that generates synthetic scanpaths on texts by leveraging pre-trained word representations and jointly embedding both the stimulus text and the fixation sequence. |
| Outcome: | The proposed model outperforms state-of-the-art models in psycholinguistic analysis and is able to exhibit human-like reading behavior. |
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| Challenge: | Existing models for augmenting language models with human scanpaths have been developed, but the potential of synthetic gaze data across NLP tasks remains unexplored. |
| Approach: | They propose to combine synthetic scanpath generation with a scanpath-augmented language model, eliminating the need for human gaze data. |
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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. |
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| Challenge: | Prior work on eye tracking and NLP reveals that human scanpaths can aid in understanding and performance of NLP models. |
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| Challenge: | Existing methods for generating text using Glauber dynamics are autoregressive, but they face a number of limitations. |
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| Challenge: | Existing discrete diffusion models for text generation have a discrepancy between training and inference. |
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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 . |
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| Challenge: | Diffusion models have shown promise in text generation, but often struggle with generating long, coherent, and contextually accurate text. |
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DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models (2023.acl-long)
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| Challenge: | Existing generative masked language models have a shared training objective, i.e., denoising. |
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Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
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| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
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