Papers with cognitive models
A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules (2023.acl-long)
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| Challenge: | Explicit linguistic knowledge encoded by rule-based morphological analyzers is expensive and non-trivial . creating such resources is tedious and requires additional efforts to extract human-interpretable patterns from them. |
| Approach: | They propose a method for automatically learning morphophonological rules of Arabic from a corpus. |
| Outcome: | The proposed approach produces a set of generalizable rules from a dataset. |
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue (2020.coling-main)
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| Challenge: | In many domains, dialogue systems need to work collaboratively with users to reconstruct meaning . this requires a system that can give targeted, effective feedback about the system’s understanding . |
| Approach: | They propose a system that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors and use crowd workers to test their approach. |
| Outcome: | The proposed system can distinguish varying color patches from distractors and elicit correct answers that the system understands. |
PATIENT-đťś“: Using Large Language Models to Simulate Patients for Training Mental Health Professionals (2024.emnlp-main)
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Ruiyi Wang, Stephanie Milani, Jamie Chiu, Jiayin Zhi, Shaun Eack, Travis Labrum, Samuel Murphy, Nev Jones, Kate Hardy, Hong Shen, Fei Fang, Zhiyu Chen
| Challenge: | Mental illness remains one of the most critical public health issues. |
| Approach: | They propose a patient simulation framework for cognitive behavior therapy training that uses large language models to act as a simulated therapy patient. |
| Outcome: | The proposed framework improves the skill acquisition and confidence of mental health trainees beyond textbooks, videos, and role-play with non-patients. |
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. |