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Kafeng Wang, Pengyang Wang, Chengzhong x...|Jan 22nd, 2022|journalArticleKafeng Wang, Pengyang Wang, Chengzhong x...Jan 22nd, 2022
Automated Feature Engineering (AFE) refers to automatically generate and select optimal feature sets for downstream tasks, which has achieved great success in real-world applications. Current AFE methods mainly focus on improving the effectiveness of the produced features, but ignoring the low-efficiency issue for large-scale deployment. Therefore, in this work, we propose a generic framework to improve the efficiency of AFE. Specifically, we construct the AFE pipeline based on reinforcement...
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Qiao Wang|Jun 21st, 2022|journalArticleQiao WangJun 21st, 2022
This study searched for open-source semantic similarity tools and evaluated their effectiveness in automated content scoring of fact-based essays written by English-as-a-Foreign-Language (EFL) learners. Fifty writing samples under a fact-based writing task from an academic English course in a Japanese university were collected and a gold standard was produced by a native expert. A shortlist of carefully selected tools, including InferSent, spaCy, DKPro, ADW, SEMILAR and Latent Semantic...
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Nigel Fernandez, Aritra Ghosh, Naiming L...|Jan 22nd, 2022|preprintNigel Fernandez, Aritra Ghosh, Naiming L...Jan 22nd, 2022
Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such as BERT and GPT as input to scoring models. Most existing approaches train a separate model for each item/question, which is suitable for scenarios such as essay scoring where items can be quite different from one another. However, these approaches have two...
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Zichao Wang, Jakob Valdez, Debshila Basu...|Jan 22nd, 2022|bookSectionZichao Wang, Jakob Valdez, Debshila Basu...Jan 22nd, 2022
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Zichao Wang, Jakob Valdez, Debshila Basu...|Jan 22nd, 2022|bookSectionZichao Wang, Jakob Valdez, Debshila Basu...Jan 22nd, 2022
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Zichao Wang, Jakob Valdez, Debshila Basu...|Jan 22nd, 2022|bookSectionZichao Wang, Jakob Valdez, Debshila Basu...Jan 22nd, 2022
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Iddo Drori, Sarah Zhang, Reece Shuttlewo...|Aug 2nd, 2022|journalArticleIddo Drori, Sarah Zhang, Reece Shuttlewo...Aug 2nd, 2022
We demonstrate that a neural network pretrained on text and fine-tuned on code solves mathematics course problems, explains solutions, and generates questions at a human level. We automatically synthesize programs using few-shot learning and OpenAI’s Codex transformer and execute them to solve course problems at 81% automatic accuracy. We curate a dataset of questions from Massachusetts Institute of Technology (MIT)’s largest mathematics courses (Single Variable and Multivariable Calculus,...
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Ying Xu, Dakuo Wang, Mo Yu|Jan 22nd, 2022|journalArticleYing Xu, Dakuo Wang, Mo YuJan 22nd, 2022
Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative...
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Rishi Bommasani, Drew A. Hudson, Ehsan A...|Jul 12th, 2022|preprintRishi Bommasani, Drew A. Hudson, Ehsan A...Jul 12th, 2022
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...