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Adopting No-Code methods to Visualize Computational Thinking 29

Adopting No-Code Methods to Visualize Computational Thinking

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29th International Conference on Computers in Education (ICCE)

作者： Hylton, Derrick Sung, Shannon Xie, Charles Spelman Coll Phys Dept Atlanta GA 30314 USA Inst Future Intelligence Natick MA 01760 USA

ISBN: (纸本)9789869721486

In this paper, we describe the application of iFlow, a no-code programming platform, in visualizing foundational computational thinking (CT) as a precursor to learning disciplinary concepts. Research shows that it is possible to learn both science concepts and computational skills through building computer simulations and solutions of problems related to natural phenomena. CT could be operationally defined as the cognitive processes involved in problem formulation, in which its solutions could be effectively carried out by an information-processing agent (Wing, 2010). We introduce two modules as examples to facilitate the visualization of computational processes that are frequently adopted in teaching physical sciences. The first module was designed for students to analyze and evaluate data, and the second module allowed students to generate simulated data and scaffold questions to predict testable outcomes. In this workshop, we will demonstrate how to use iFlow to teach data analysis and problem evaluation that takes advantage of the no-code programming by means of the functional blocks, which contain operational codes to vividly visualize the input, process, and output.

关键词： Computational thinking in STEM education graph-based programming no-code programming visualizing computation

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Should You Fine-Tune BERT for Automated Essay Scoring? 15

Should You Fine-Tune BERT for Automated Essay Scoring?

引用

15th workshop on Innovative Use of NLP for Building Educational Applications

作者： Mayfield, Elijah Black, Alan W. Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781952148187

Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks;older strategies like bag-ofwords features and linear models have fallen out of favor. Here we investigate whether, in automated essay scoring (AES) research, deep neural models are an appropriate technological choice. We find that fine-tuning BERT produces similar performance to classical models at significant additional cost. We argue that while state-of-the-art strategies do match existing best results, they come with opportunity costs in computational resources. We conclude with a review of promising areas for research on student essays where the unique characteristics of Transformers may provide benefits over classical methods to justify the costs.

关键词： natural language processing systems

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Using natural language processing for supply chain mapping: A systematic review of current approaches 5

Using natural language processing for supply chain mapping: ...

引用

5th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Main Conference, COLINS 2021

作者： Schöpper, Henning Wolfgang, Kersten Hamburg University of Technology Am Schwarzenberg-Campus 4 Hamburg Germany

Purpose: The COVID-19 crisis has shown that the global supply chains are not as resilient as expected. First investigations indicate that the main contributing factor is a lack of visibility into the supply chain's lower tiers. Simultaneously, the willingness to share data in the supply chain is low as companies mainly consider their data as proprietary. However, large amounts of data are available on the internet. The amount of this data is steadily increasing;however, the problem remains, that this data is hardly structured. Therefore, this paper investigates current approaches to use this data for supply chain transparency and derives further research directions. Methodology: The paper uses a systematic review of the literature followed by content analysis. The research process further follows established frameworks in the literature and is subdivided into distinct stages. Findings: Descriptive and clustering results show a fragmented research field, where current approaches disconnect from prior research. We classify the methods using a simple taxonomy and show developments from rule-based to supervised techniques and horizontal to vertical mining approaches. The techniques with rule-based-matching procedures mainly suffer from low recall. The current approaches do not satisfy yet essential requirements on supply chain mapping based on natural language. Originality: To the best of the authors' knowledge, no prior research has been attempted to review textual data usage for supply chain mapping. Therefore, this paper's main contribution is to fill this gap and add further evidence to the use of data-driven supply chain management methods. © 2021 CEUR-WS. All rights reserved.

关键词： Mapping

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ChiSquareX at Textgraphs 2020 Shared Task: Leveraging Pre-trained language Models for Explanation Regeneration 14

ChiSquareX at TextGraphs 2020 Shared Task: Leveraging Pre-tr...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Pawate, Aditya Girish Chandak, Devansh Madhavan, Varun IIT Kharagpur India IIT Bombay India

ISBN: (纸本)9781952148422

In this work, we describe the system developed by a group of undergraduates from the Indian Institutes of Technology, for the Shared Task at Textgraphs-14 on Multi-Hop Inference Explanation Regeneration (Jansen and Ustalov, 2020). The shared task required participants to develop methods to reconstruct gold explanations for elementary science questions from the WorldTree Corpus (Xie et al., 2020). Although our research was not funded by any organization and all the models were trained on freely available tools like Google Colab which restricted our computational capabilities, we have managed to achieve noteworthy results placing ourselves in the 4th place with a MAP score of 0.49021 in the evaluation leaderboard and 0.5062 MAP score on the post-evaluation-phase leaderboard using RoBERTa. We incorporated some of the methods proposed in the previous edition of Textgraphs-13 (Chia et al., 2019), which proved to be very effective, improved upon them, and built a model on top of it using powerful state-of-the-art pre-trained language models like RoBERTa (Liu et al., 2019), BART (Lewis et al., 2020), SciBERT (Beltagy et al., 2019) among others. Further optimization of our work can be done with the availability of better computational resources. © COLING *** rights reserved.

关键词： Computational linguistics

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Multiple Scale Visualization of Electronic Health Records to Support Finding Medical Narratives

Multiple Scale Visualization of Electronic Health Records to...

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2021 Eurographics workshop on Visual Computing for Biology and Medicine, VCBM 2021

作者： van der Linden, S. van Wijk, J.J. Funk, M. Netherlands

ISBN: (纸本)9783038681403

Electronic Health Records (EHRs) contain rich medical information about patients, possibly hundreds of notes, lab results, images and other information. Doctors can easily be overwhelmed by this wealth of information. For their daily work, they need to derive narratives from all this information to get insights into the main issues of their patients. Standard solutions show all the information in linear lists, often leading to cognitive overload;research solutions provide timelines and relations between the notes but provide too much fragmented information. We propose MEDeNAR, a system for enabling medical professionals to obtain insights from EHRs based on the different tasks in their workflow. The key aspects of our system are the introduction of an intermediate level that summarizes the information using clustering and NLP methods. The results are visualized along a timeline and provide easy access to the detailed descriptions in notes and lab results at the EHR level. We designed the system using an iterative design process in collaboration with 18 doctors, two nurses and 14 domain experts. During the final evaluation, the doctors ranked our system higher than a standard baseline solution and a variation for the used NLP methods. © 2021 The Author(s) Eurographics proceedings © 2021 The Eurographics Association.

关键词： natural language processing systems

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Considering Likelihood in NLP Classification Explanations with Occlusion and language Modeling 58

Considering Likelihood in NLP Classification Explanations wi...

引用

58th Annual Meeting of the Association-for-Computational-Linguistics (ACL)

作者： Harbecke, David Alt, Christoph German Res Ctr Artificial Intelligence DFKI Berlin Germany

ISBN: (纸本)9781952148033

Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions. Occlusion is a well established method that provides explanations on discrete language data, e.g. by removing a language unit from an input and measuring the impact on a model's decision. We argue that current occlusion-based methods often produce invalid or syntactically incorrect language data, neglecting the improved abilities of recent NLP models. Furthermore, gradient-based explanation methods disregard the discrete distribution of data in NLP. Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input. We lay out a theoretical foundation that alleviates these weaknesses of other explanation methods in NLP and provide results that underline the importance of considering data likelihood in occlusion-based explanation.(1)

关键词： natural language processing systems

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An empirical investigation of neural methods for content scoring of science explanations 15

An empirical investigation of neural methods for content sco...

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15th workshop on Innovative Use of NLP for Building Educational Applications

作者： Riordan, Brian Bichler, Sarah Bradford, Allison Chen, Jennifer King Wiley, Korah Gerard, Libby Linn, Marcia C. ETS Princeton NJ 08540 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781952148187

With the widespread adoption of the Next Generation Science Standards (NGSS), science teachers and online learning environments face the challenge of evaluating students' integration of different dimensions of science learning. Recent advances in representation learning in natural language processing have proven effective across many natural language processing tasks, but a rigorous evaluation of the relative merits of these methods for scoring complex constructed response formative assessments has not previously been carried out. We present a detailed empirical investigation of feature-based, recurrent neural network, and pre-trained transformer models on scoring content in real-world formative assessment data. We demonstrate that recent neural methods can rival or exceed the performance of feature-based methods. We also provide evidence that different classes of neural models take advantage of different learning cues, and pre-trained transformer models may be more robust to spurious, dataset-specific learning cues, better reflecting scoring rubrics.

关键词： Recurrent neural networks

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Applying natural language processing Models to Create Recommendations for Professional Skills Development 6

Applying Natural Language Processing Models to Create Recomm...

引用

6th International Scientific and Practical Conference "Distance Learning Technologies", DLT 2021

作者： Kosarava, Katsiaryna Yanka Kupala State University of Grodno 22 Ozheshko str. Grodno230023 Belarus

The article examines the problem of applying intellectual methods and models for the automatic extraction of professional skills and competencies from the descriptions of academic disciplines in Russian. The accuracy of pre-trained multilingual models of distributed vector representation LASER and SBERT is investigated. The process of collecting and preprocessing a dataset for creating and training skills classification models is described. A neural network model of skills classification based on a bidirectional recurrent neural network with long-shortterm memory is described and trained. It was trained on data prepared two different ways: 1) skills were separated on "soft skills" and "hard skills", the last one was not translated into Russian;2) "soft skills" and "hard skills" were treated as one class and were translated into Russian. Learning curves on validation data are presented. They show high accuracy of trained models up to 99%. The test accuracy of the models was analyzed employing confusion matrices and the results show a fairly high performance of the model. an example of skills extraction from an academic discipline using trained models is given. © 2021 CEUR-WS. All rights reserved.

关键词： Classification (of information)

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Using named entity recognition to identify personification constructions in an english Spanish intermodal corpus of the EP committee on petitions

Using named entity recognition to identify personification c...

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2021 Doctoral Symposium on natural language processing from the PLN.net Network, PLNnet-DS-2021

作者： Rodas, Fernando Sánchez University of Málaga Campus de Teatinos s/n Málaga29071 Spain

This paper presents a PhD project which tests the effectiveness of NLP-based methods to extract and analyze large amounts of data from translation and interpreting corpora. More specifically, Named Entity Recognition (NER) applications are used in combination with an intermodal corpus of EU texts (that is, a multilingual corpus which contain all possible variations of mediated and non-mediated discourse) in order to identify personification constructions, especially those related to organizations. From the point of view of Construction Grammar (CxG), personifications are argument-structure constructions loaded with relational meaning, which makes them valuable data to feed Machine Translation (MT) and Machine Interpreting (MI) systems or related electronic tools used by translation and interpreting professionals in the briefing preparation phase. In the future, we expect that the compiled intermodal corpus (named PETIMOD) and the NLP techniques can be used to study further types of constructions in institutional discourse, which would be an important contribution not only to corpus-based translation and interpreting studies, but also to CxG itself. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： natural language processing systems

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AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained language Models 3

AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End ...

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3rd workshop on natural language processing for Conversational AI, NLP4ConvAI 2021

作者： Kulhánek, Jonáš Hudeček, Vojtěch Nekvinda, Tomáš Dušek, Ondřej Charles University Faculty of Mathematics and Physics Prague Czech Republic Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Republic Czech Technical University in Prague Faculty of Electrical Engineering Czech Republic

ISBN: (纸本)9781954085862

Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via backtranslation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation. © 2021 Association for Computational Linguistics.

关键词： Modeling languages

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