Klinton Bicknell


2020

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Simultaneous Translation and Paraphrase for Language Education
Stephen Mayhew | Klinton Bicknell | Chris Brust | Bill McDowell | Will Monroe | Burr Settles
Proceedings of the Fourth Workshop on Neural Generation and Translation

We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology. We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then fine-tune with in-domain data, sampled according to our provided learner response frequencies.

2019

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Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning
Nicole Mirea | Klinton Bicknell
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level.

2018

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Predictive power of word surprisal for reading times is a linear function of language model quality
Adam Goodkind | Klinton Bicknell
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)

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Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics
Adam Goodkind | Michelle Lee | Gary E. Martin | Molly Losh | Klinton Bicknell
Proceedings of the Society for Computation in Linguistics (SCiL) 2018

2014

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Nonparametric Learning of Phonological Constraints in Optimality Theory
Gabriel Doyle | Klinton Bicknell | Roger Levy
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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A model of generalization in distributional learning of phonetic categories
Bozena Pajak | Klinton Bicknell | Roger Levy
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

2012

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Why long words take longer to read: the role of uncertainty about word length
Klinton Bicknell | Roger Levy
Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2012)

2010

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A Rational Model of Eye Movement Control in Reading
Klinton Bicknell | Roger Levy
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Computational psycholinguistics
Roger Levy | Klinton Bicknell | Nathaniel Smith
NAACL HLT 2010 Tutorial Abstracts

2009

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A model of local coherence effects in human sentence processing as consequences of updates from bottom-up prior to posterior beliefs
Klinton Bicknell | Roger Levy
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics