Casey Kennington

Also published as: Casey Redd Kennington


2021

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

We offer 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 Unit model and give an example application using the Localized Narratives dataset, then offer avenues for future research.

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Natural Language Processing for Computer Scientists and Data Scientists at a Large State University
Casey Kennington
Proceedings of the Fifth Workshop on Teaching NLP

The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it’s a field that should be accessible to anyone who has a sufficient background. In this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.

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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

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 entities and embodied states. To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers trained on images for the BERT vocabulary. We pre-trained the ELECTRA model and fine-tuned the RoBERTa model with these two sources of information then evaluate using the established GLUE benchmark and the Visual Dialog benchmark. We find that enriching language models with the Lancaster norms and image vectors improves results in both tasks, with some implications for robust language models that capture holistic linguistic meaning in a language learning context.

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Spellchecking for Children in Web Search: a Natural Language Interface Case-study
Casey Kennington | Jerry Alan Fails | Katherine Landau Wright | Maria Soledad Pera
Proceedings of the First Workshop on Bridging Human–Computer Interaction and Natural Language Processing

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 context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.

2020

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Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Olivier Pietquin | Smaranda Muresan | Vivian Chen | Casey Kennington | David Vandyke | Nina Dethlefs | Koji Inoue | Erik Ekstedt | Stefan Ultes
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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

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 people in settings for language use, such as spoken dialogue. In a live interactive study, we test the hypothesis that emotional displays are a viable solution to the cold-start problem of how to communicate without relying on language the robot does not–indeed, cannot–yet know. We explain our modular system that can autonomously learn word groundings through interaction and show through a user study with 21 participants that emotional displays improve the quantity and quality of the inputs provided to the robot.

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rrSDS: Towards a Robot-ready Spoken Dialogue System
Casey Kennington | Daniele Moro | Lucas Marchand | Jake Carns | David McNeill
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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 ReTiCo incremental framework. We outline the incremental and multimodal modules and how their computation can be distributed. We demonstrate the power and flexibility of our robot-ready spoken dialogue system to be integrated with almost any robot.

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Evaluating and Improving Child-Directed Automatic Speech Recognition
Eric Booth | Jake Carns | Casey Kennington | Nader Rafla
Proceedings of the 12th Language Resources and Evaluation Conference

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 child-directed speech recognition by training the recent DeepSpeech2 model on adult data, then apply additional tuning to varied amounts of child speech data. We evaluate our model using the CMU Kids dataset as well as our own recordings of child-directed prompts. The results from our experiment show that even a small amount of child audio data improves significantly over a baseline of adult-only or child-only trained models. We report a final general Word-Error-Rate of 29% over a baseline of 62% that uses the adult-trained model. Our analyses show that our model adapts quickly using a small amount of data and that the general child model works better than school grade-specific models. We make available our trained model and our data collection tool.

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KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker
Brody Downs | Oghenemaro Anuyah | Aprajita Shukla | Jerry Alan Fails | Sole Pera | Katherine Wright | Casey Kennington
Proceedings of the 12th Language Resources and Evaluation Conference

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) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model’s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.

2018

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Predicting Perceived Age: Both Language Ability and Appearance are Important
Sarah Plane | Ariel Marvasti | Tyler Egan | Casey Kennington
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.

2016

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Real-Time Understanding of Complex Discriminative Scene Descriptions
Ramesh Manuvinakurike | Casey Kennington | David DeVault | David Schlangen
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Supporting Spoken Assistant Systems with a Graphical User Interface that Signals Incremental Understanding and Prediction State
Casey Kennington | David Schlangen
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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PentoRef: A Corpus of Spoken References in Task-oriented Dialogues
Sina Zarrieß | Julian Hough | Casey Kennington | Ramesh Manuvinakurike | David DeVault | Raquel Fernández | David Schlangen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings. The corpus is multilingual, with English and German sections, and overall comprises more than 20000 utterances. The dialogues are fully transcribed and annotated with referring expressions mapped to objects in corresponding visual scenes, which makes the corpus a rich resource for research on spoken referring expressions in generation and resolution. The corpus includes several sub-corpora that correspond to different dialogue situations where parameters related to interactivity, visual access, and verbal channel have been manipulated in systematic ways. The corpus thus lends itself to very targeted studies of reference in spontaneous dialogue.

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Resolving References to Objects in Photographs using the Words-As-Classifiers Model
David Schlangen | Sina Zarrieß | Casey Kennington
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
Pierre Lison | Casey Kennington
Proceedings of ACL-2016 System Demonstrations

2015

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Incrementally Tracking Reference in Human/Human Dialogue Using Linguistic and Extra-Linguistic Information
Casey Kennington | Ryu Iida | Takenobu Tokunaga | David Schlangen
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Simple Learning and Compositional Application of Perceptually Grounded Word Meanings for Incremental Reference Resolution
Casey Kennington | David Schlangen
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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A Discriminative Model for Perceptually-Grounded Incremental Reference Resolution
Casey Kennington | Livia Dia | David Schlangen
Proceedings of the 11th International Conference on Computational Semantics

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Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches
Julian Hough | Casey Kennington | David Schlangen | Jonathan Ginzburg
Proceedings of the 11th International Conference on Computational Semantics

2014

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Situationally Aware In-Car Information Presentation Using Incremental Speech Generation: Safer, and More Effective
Spyros Kousidis | Casey Kennington | Timo Baumann | Hendrik Buschmeier | Stefan Kopp | David Schlangen
Proceedings of the EACL 2014 Workshop on Dialogue in Motion

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InproTKs: A Toolkit for Incremental Situated Processing
Casey Kennington | Spyros Kousidis | David Schlangen
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Situated Incremental Natural Language Understanding using a Multimodal, Linguistically-driven Update Model
Casey Kennington | Spyros Kousidis | David Schlangen
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Interpreting Situated Dialogue Utterances: an Update Model that Uses Speech, Gaze, and Gesture Information
Casey Kennington | Spyros Kousidis | David Schlangen
Proceedings of the SIGDIAL 2013 Conference

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Investigating speaker gaze and pointing behaviour in human-computer interaction with the mint.tools collection
Spyros Kousidis | Casey Kennington | David Schlangen
Proceedings of the SIGDIAL 2013 Conference

2012

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Suffix Trees as Language Models
Casey Redd Kennington | Martin Kay | Annemarie Friedrich
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Suffix trees are data structures that can be used to index a corpus. In this paper, we explore how some properties of suffix trees naturally provide the functionality of an n-gram language model with variable n. We explain these properties of suffix trees, which we leverage for our Suffix Tree Language Model (STLM) implementation and explain how a suffix tree implicitly contains the data needed for n-gram language modeling. We also discuss the kinds of smoothing techniques appropriate to such a model. We then show that our suffix-tree language model implementation is competitive when compared to the state-of-the-art language model SRILM (Stolke, 2002) in statistical machine translation experiments.

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Markov Logic Networks for Situated Incremental Natural Language Understanding
Casey Kennington | David Schlangen
Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2011

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DFKI Hybrid Machine Translation System for WMT 2011 - On the Integration of SMT and RBMT
Jia Xu | Hans Uszkoreit | Casey Kennington | David Vilar | Xiaojun Zhang
Proceedings of the Sixth Workshop on Statistical Machine Translation

2008

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Elicited Imitation as an Oral Proficiency Measure with ASR Scoring
C. Ray Graham | Deryle Lonsdale | Casey Kennington | Aaron Johnson | Jeremiah McGhee
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper discusses development and evaluation of a practical, valid and reliable instrument for evaluating the spoken language abilities of second-language (L2) learners of English. First we sketch the theory and history behind elicited imitation (EI) tests and the renewed interest in them. Then we present how we developed a new test based on various language resources, and administered it to a few hundred students of varying levels. The students were also scored using standard evaluation techniques, and the EI results were compared to more traditionally derived scores. We also sketch how we developed a new integrated tool that allows the session recordings of the EI data to be analyzed with a widely-used automatic speech recognition (ASR) engine. We discuss the promising results of the ASR engine’s processing of these files and how they correlated with human scoring of the same items. We indicate how the integrated tool will be used in the future. Further development plans and prospects for follow-on work round out the discussion.