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43 resources
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Hassan Zeineddine, Udo Braendle, Assaad ...|Jan 10th, 2021|journalArticleHassan Zeineddine, Udo Braendle, Assaad ...Jan 10th, 2021
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Sami Baral, Anthony F. Botelho, John A. ...|Mar 10th, 2021|conferencePaperSami Baral, Anthony F. Botelho, John A. ...Mar 10th, 2021
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Randy Elliot Bennett, Mo Zhang, Sandip S...|Mar 10th, 2021|journalArticleRandy Elliot Bennett, Mo Zhang, Sandip S...Mar 10th, 2021
This study examined differences in the composition processes used by educationally at-risk males and females who wrote essays as part of a high-school equivalency examination. Over 30,000 individuals were assessed, each taking one of 12 forms of the examination’s language arts writing subtest in 23 US states. Writing processes were inferred using features extracted from keystroke logs and aggregated into seven composite indicators. Results showed that females earned higher essay and total...
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Guembe Blessing, Ambrose Azeta, Sanjay M...|Mar 10th, 2021|bookSectionGuembe Blessing, Ambrose Azeta, Sanjay M...Mar 10th, 2021
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Rishi Bommasani, Drew A. Hudson, Ehsan A...|Mar 10th, 2021|journalArticleRishi Bommasani, Drew A. Hudson, Ehsan A...Mar 10th, 2021
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical...
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Elizabeth Clark, Tal August, Sofia Serra...|Mar 10th, 2021|preprintElizabeth Clark, Tal August, Sofia Serra...Mar 10th, 2021
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore...
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Elizabeth Clark, Tal August, Sofia Serra...|Mar 10th, 2021|preprintElizabeth Clark, Tal August, Sofia Serra...Mar 10th, 2021
Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recipes). We find that, without training, evaluators distinguished between GPT3- and human-authored text at random chance level. We explore...
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Saad Khan, Jesse Hamer, Tiago Almeida|Mar 10th, 2021|conferencePaperSaad Khan, Jesse Hamer, Tiago AlmeidaMar 10th, 2021
We present Generate, a AI-human hybrid system to help education content creators interactively generate assessment content in an efficient and scalable manner. Our system integrates advanced natural language generation (NLG) approaches with subject matter expertise of assessment developers to efficiently generate a large number of highly customized and valid assessment items. We utilize the powerful Transformer architecture which is capable of leveraging substantive pretraining on several...
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Vivekanandan S. Kumar, David Boulanger|Sep 10th, 2021|journalArticleVivekanandan S. Kumar, David BoulangerSep 10th, 2021
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Michael McTear, Michael McTear|Mar 10th, 2021|bookSectionMichael McTear, Michael McTearMar 10th, 2021
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Gabriel Oliveira dos Santos, Esther Luna...|Mar 10th, 2021|preprintGabriel Oliveira dos Santos, Esther Luna...Mar 10th, 2021
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high...
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University of Wolverhampton, UK, Hadeel ...|Mar 10th, 2021|conferencePaperUniversity of Wolverhampton, UK, Hadeel ...Mar 10th, 2021
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Masaki Uto|Jul 10th, 2021|journalArticleMasaki UtoJul 10th, 2021
Abstract Automated essay scoring (AES) is the task of automatically assigning scores to essays as an alternative to grading by humans. Although traditional AES models typically rely on manually designed features, deep neural network (DNN)-based AES models that obviate the need for feature engineering have recently attracted increased attention. Various DNN-AES models with different characteristics have been proposed over the past few years. To our knowledge, however, no study has...
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Cong Wang, Xiufeng Liu, Lei Wang|Apr 10th, 2021|journalArticleCong Wang, Xiufeng Liu, Lei WangApr 10th, 2021
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Tianqi Wang, Hiroaki Funayama, Hiroki Ou...|Mar 10th, 2021|journalArticleTianqi Wang, Hiroaki Funayama, Hiroki Ou...Mar 10th, 2021
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Zichao Wang, Andrew Lan, Richard Baraniu...|Mar 10th, 2021|conferencePaperZichao Wang, Andrew Lan, Richard Baraniu...Mar 10th, 2021
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Kim Christopher Williamson, René F. Kizi...|Mar 10th, 2021|conferencePaperKim Christopher Williamson, René F. Kizi...Mar 10th, 2021
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Mike Wu, Noah Goodman, Chris Piech|Mar 10th, 2021|journalArticleMike Wu, Noah Goodman, Chris PiechMar 10th, 2021
High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale. While this feedback could in principle be automated, supervised approaches to predicting the correct feedback are bottlenecked by the intractability of annotating large quantities of student code. In this paper, we instead frame the problem of providing feedback as few-shot classification, where a meta-learner adapts to give feedback to student code on a new programming...
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Khensani Xivuri, Hossana Twinomurinzi, D...|Mar 10th, 2021|bookSectionKhensani Xivuri, Hossana Twinomurinzi, D...Mar 10th, 2021
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Jing Xu, Da Ju, Margaret Li|Mar 10th, 2021|conferencePaperJing Xu, Da Ju, Margaret LiMar 10th, 2021