SelectedWorks - Casey R. Kennington

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Selected Works of Casey R. Kennington

Assistant Professor, Department of Computer Science

Dr. Casey R. Kennington is an Associate Professor in the Department of Computer Science. Focusing on machine learning and human-robot interaction, his research specialty falls with dialogue systems in terms of computational linguistics and natural language processing. Dr. Kennington received his Ph.D. in Linguistics from the Universität Bielefeld in Germany, his M.S. in Cognitive Science from the Université Nancy 2 (now the Université de Lorraine) in France, and an M.S. in Computational Linguistics from the Universität des Saarlandes.

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About Casey R. Kennington

Dr. Casey R. Kennington is an Associate Professor in the Department of Computer Science. Focusing on machine learning and human-robot interaction, his research specialty falls with dialogue systems in terms of computational linguistics and natural language processing. Dr. Kennington received his Ph.D. in Linguistics from the Universität Bielefeld in Germany, his M.S. in Cognitive Science from the Université Nancy 2 (now the Université de Lorraine) in France, and an M.S. in Computational Linguistics from the Universität des Saarlandes.

Articles (4)

KidSpell: Making a Difference in Spellchecking for Children (Brody Downs, Maria Soledad Pera, Katherine Landau Wright, et al.) International Journal of Child-Computer Interaction (2022)

Children’s ability to spell effectively is a major barrier to using search engines successfully. While search engines make use of spellcheckers to provide spelling corrections to their users, they are designed for more traditional users ...

Spoken Language Interaction with Robots: Recommendations for Future Research (Casey Kennington) Computer Speech & Language (2022)

With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language ...

In-Game Social Interactions to Facilitate ESL Students' Morphological Awareness, Language and Literacy Skills (Yolanda A. Rankin, Sana Tibi, Casey Kennington, et al.) Proceedings of the ACM on Human-Computer Interaction (2021)

Video games that require players to utilize a target or second language to complete tasks have emerged as alternative pedagogical tools for Second Language Acquisition (SLA). With the exception of vocabulary acquisition, much of the ...

Situated Incremental Natural Language Understanding Using Markov Logic Networks (Casey Kennington and David Schlangen) Computer Speech & Language (2014)

We present work on understanding natural language in a situated domain in an incremental, word-by-word fashion. We explore a set of models specified as Markov Logic Networks and show that a model that has access ...

SemDial 2019 LondonLogue, Proceedings of the 23rd Workshop on the Semantics and Pragmatics of Dialogue, London 4-6 September 2019 (Christine Howes, Julian Hough and Casey Kennington) (2019)

LondonLogue brings the SemDial Workshop on the Semantics and Pragmatics of Dialogue to Queen Mary University of London for the first time, though back to London, as the 12th meeting – LONDIAL – took place ...

Evaluating Automatic Speech Recognition and Natural Language Understanding in an Incremental Setting (Ryan Whetten, Enoch Levandovsky, Mir Tahsin Imtiaz, et al.) Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue - Full Papers (2023)

Spoken dialogue systems enable people to interact with machines using speech, many of which involve the use of automatic speech recognition and language understanding in order to react to and determine a decision about how ...

Exploring Transformers as Compact, Data-Efficient Language Models (Clayton Fields and Casey Kennington) Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) (2023)

Large scale transformer models, trained with massive datasets have become the standard in natural language processing. The huge size of most transformers make research with these models impossible for those with limited computational resources. Additionally, ...

Tiny Language Models Enriched with Multimodal Knowledge from Multiplex Networks (Clayton Fields, Osama Natouf, Andrew McMains, et al.) Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning (2023)

Large transformer language models trained exclusively on massive quantities of text are now the standard in NLP. In addition to the impractical amounts of data used to train them, they require enormous computational resources for ...

The Symbol Grounding Problem re-framed as concreteness-abstractness learned through spoken interaction (Casey Redd Kennington and Osama Natouf) Proceedings of the 26th Workshop on the Semantics and Pragmatics of Dialogue - Full Papers (2022)

The Symbol Grounding Problem points out that the underlying mechanisms of computation are symbolic and, therefore, missing crucial information when they are used for processing natural language until they are somehow able to perceive the ...

Incremental Unit Networks for Distributed, Symbolic Multimodal Processing and Representation (Mir Tahsin Imtiaz and Casey Kennington) HCII 2022: Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design (2022)

Incremental dialogue processing has been an important topic in spoken dialogue systems research, but the broader research community that makes use of language interaction (e.g., chatbots, conversational AI, spoken interaction with robots) have not adopted ...

Supercalifragilisticexpialidocious: Why Using the “Right” Readability Formula in Children’s Web Search Matters (Garrett Allen, Ashlee Milton, Katherine Landau Wright, et al.) Advances in Informational Retrieval (2022)

Readability is a core component of information retrieval (IR) tools as the complexity of a resource directly affects its relevance: a resource is only of use if the user can comprehend it. Even so, the ...

Symbol and Communicative Grounding Through Object Permanence with a Mobile Robot (Josue Torres-Fonseca, Catherine Henry and Casey Kennington) Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue (2022)

Object permanence is the ability to form and recall mental representations of objects even when they are not in view. Despite being a crucial developmental step for children, object permanence has had only some exploration ...

Understanding Intention for Machine Theory of Mind: A Position Paper (Casey Kennington) 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (2022)

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find ...

Engage!: Co-Designing Search Engine Result Pages to Foster Interactions (Garrett Allen, Ben Peterson, Dhanush Kumar Ratakonda, et al.) IDC '21: Interaction Design and Children (2021)

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session ...

Spellchecking for Children in Web Search: A Natural Language Interface Case-Study (Casey Kennington, Jerry Alan Fails, Katherine Landau Wright, et al.) Proceedings of the First Workshop on Bridging Human-Computer Interaction and Natural Language Processing (2021)

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the ...

BiGBERT: Classifying Educational Web Resources for Kindergarten-12th Grades (Garrett Allen, Brody Downs, Aprajita Shukla, et al.) ECIR 2021: Advances in Information Retrieval (2021)

In this paper, we present BiGBERT, a deep learning model that simultaneously examines URLs and snippets from web resources to determine their alignment with children’s educational standards. Preliminary results inferred from ablation studies and comparison ...

Enriching Language Models with Visually-Grounded Word Vectors and the Lancaster Sensorimotor Norms (Casey Kennington) Proceedings of the 25th Conference on Computational Natural Language Learning (2021)

Language models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical ...

Incremental Unit Networks for Multimodal, Fine-Grained Information State Representation (Casey Kennington and David Schlangen) Proceedings of the 1st Workshop on Multimodal Semantic Representations (MMSR) (2021)

We offer a sketch of a fine-grained information state annotation scheme that follows directly from the Incremental Unit abstract model of dialogue processing when used within a multimodal, co-located, interactive setting. We explain the Incremental ...

Learning Word Groundings from Humans Facilitated by Robot Emotional Displays (David McNeill and Casey Kennington) Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue (2020)

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from ...

Semantics with Feeling: Emotions for Abstract Embedding, Affect for Concrete Grounding (Daniele Moro, Gerardo Caracas, David McNeill, et al.) Proceedings of the 24th Workshop on the Semantics and Pragmatics of Dialogue (2020)

An important yet underexplored aspect of meaning in both distributional and grounded models of semantics is emotion. In this paper, we explore how emotion can be predicted from descriptions of robot behaviors represented with embeddings. ...

rrSDS: Towards a Robot-Ready Spoken Dialogue System (Casey Kennington, Daniele Moro, Lucas Marchand, et al.) Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue (2020)

Spoken interaction with a physical robot requires a dialogue system that is modular, multimodal, distributive, incremental and temporally aligned. In this demo paper, we make significant contributions towards fulfilling these requirements by expanding upon the ...

Guiding the Selection of Child Spellchecker Suggestions Using Audio and Visual Cues (Brody Downs, Aprajita Shukla, Mikey Krentz, et al.) IDC '20: Proceedings of the Interaction Design and Children Conference (2020)

Spellchecking functionality embedded in existing search tools can assist children by offering a list of spelling alternatives when a spelling error is detected. Unfortunately, children tend to generally select the first alternative when presented with ...

Evaluating and Improving Child-Directed Automatic Speech Recognition (Eric Booth, Jake Carns, Casey Kennington, et al.) LREC 2020, Twelfth International Conference on Language Resources and Evaluation (2020)

Speech recognition has seen dramatic improvements in the last decade, though those improvements have focused primarily on adult speech. In this paper, we assess child-directed speech recognition and leverage a transfer learning approach to improve ...

KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker (Brody Downs, Oghenemaro Anuyah, Aprajita Shukla, et al.) LREC 2020, Twelfth International Conference on Language Resources and Evaluation (2020)

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) ...

Predicting Human Interpretations of Affect and Valence in a Social Robot (David McNeill and Casey Kennington) Proceedings of Robotics: Science and Systems (2019)

In this paper we seek to understand how people interpret a social robot’s performance of an emotion, what we term ‘affective display,’ and the positive or negative valence of that affect. To this end, we ...