Papers by Casey Kennington

6 papers
Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues (2024.lrec-main)

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Challenge: Existing large language models can be fine-tuned offline but are large and resource-intensive.
Approach: They propose to use a simple reference resolver to simulate a conceptual pact process over time with different conversation pairs.
Outcome: The proposed model performs better than a pre-trained model with exhaustive retraining after each prediction, while being more transparent, faster and less resource-intensive.
Incorporating Word-level Phonemic Decoding into Readability Assessment (2024.lrec-main)

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Challenge: a recent study suggests that automatic readability assessment is not able to provide interpretability for teachers and educators.
Approach: They propose to incorporate phonetic and orthographic features into automatic readability assessment by handcrafted feature sets.
Outcome: a new feature set shows comparable performance to larger feature sets on grade-level classification in english . authors say the model improves on multiple readability datasets but lacks interpretability .
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors (2022.lrec-1)

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Challenge: HADREB datasets explore how humans perceive robot emotional states . emotions are a fundamental part of the human language system and are used as scaffolding for language learning .
Approach: They present a dataset of human appraisals and English descriptions of robot emotional behaviors . they use mistyrobotics mist and digital dream labs cozmo robots to analyze the data .
Outcome: The proposed dataset examines how humans perceive robot emotional states and how they relate to human language.
KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker (2020.lrec-1)

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Challenge: Existing spellcheckers are tuned to the needs of adults and are unsatisfactory for children due to their varying cognitive capabilities.
Approach: They propose a model that maps misspelled words and spelling suggestions to their phonetic keys and a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word.
Outcome: The proposed model outperforms existing spellcheckers in a number of offline experiments using existing and novel datasets.
SafetyALFRED: Evaluating Safety-Conscious Planning of Vision Language Models (2026.findings-acl)

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Challenge: Existing safety evaluations focus on hazard recognition through disembodied question answering (QA) settings, but lack a critical gap in evaluating an agent.
Approach: They evaluate multimodal large language models with six categories of kitchen hazards . they propose a safety-based approach that prioritizes multi-step corrective actions .
Outcome: The proposed model can recognize hazards in QA settings, but average mitigation success rates are low . the proposed model is based on the embodied agent benchmark ALFRED .
Evaluating and Improving Child-Directed Automatic Speech Recognition (2020.lrec-1)

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Challenge: a recent study shows that adult speech recognition systems are lagging behind child models due to the fact that children's vocal tracts are smaller than adults .
Approach: They evaluate a model that trains on adult data and apply additional tuning to varied amounts of child speech data to improve child-directed speech recognition.
Outcome: The proposed model improves over baseline models using child data and small amounts of child audio data.

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