[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117945-en":3,"doc-seo-117945-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117945,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Machine-learning based automatic assessment of communication in interpreting","Communication assessment in interpreting evaluates messages combining verbal and nonverbal signals, yet reliable automated scoring remains challenging due to identifying which automatically extracted parameters best predict outcomes. This study develops and tests machine-learning models to automatically assess communication in English/Chinese interpreting by building predictive algorithms from delivery features and applying translation-quality estimation for information assessment. Models using K-nearest neighbour and support vector machine are compared, with the SVM approach achieving 62.96% accuracy. The pass level can be predicted for screening with minimal human evaluation, enabling fast feedback for learners and reduced workload for educators.","TYPE Original Research PUBLISHED 24 January 2023  \nDOI 10. 3389/fcomm.2023.1047753  \nOPEN ACCESS  \nEDITED BY  \nAntonio Benítez-Burraco, University of Seville, Spain  \nREVIEWED BY  \nM. Dolores Jiménez-López, University of Rovira i Virgili, Spain Adrià Torrens-Urrutia, University of Rovira i Virgili, Spain  \n*CORRESPONDENCE  \nXiaoman Wang  \n [mlxwang@leeds.ac.uk](mlxwang@leeds.ac.uk)  \nSPECIALTY SECTION  \nThis article was submitted to Language Sciences, a section of the journal Frontiers in Communication  \nRECEIVED 22 September 2022  \nACCEPTED 09 January 2023  \nPUBLISHED 24 January 2023  \nCITATION  \nWang X and Yuan L (2023) Machine-learning based automatic assessment of communication in interpreting.  \nFront. Commun. 8:1047753 .  \ndoi: 10.3389/fcomm.2023.1047753  \nCOPYRIGHT  \n© 2023 Wang and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine-learning based automatic assessment of communication in interpreting  \nXiaoman Wang* and Lu Yuan  \nSchool of Language, Culture and Society, University of Leeds, Leeds, United Kingdom  \nCommunication assessment in interpreting has developed into an area with new models and continues to receive growing attention in recent years. The process refers to the assessment of messages composed of both “verbal” and “nonverbal” signals. A few relevant studies revolving around automatic scoring investigated the assessment of fluency based on objective temporal measures, and the correlation between the machine translation metrics and human scores. There is no research exploring machine-learning-based automatic scoring in-depth integrating parameters of delivery and information. What remains fundamentally challenging to demonstrate is which parameters, extracted through an automatic methodology, predict more reliable results. This study presents an original study with the aim to propose and test a machine learning approach to automatically assess communication in English/Chinese interpreting. It proposes to build predictive models using machine learning algorithms, extracting parameters for delivery, and applying a translation quality estimation model for information assessment to describe the final model. It employs the K-nearest neighbour algorithm and support vector machine for further analysis. It is found that the best machine-learning model built with all features by Support Vector Machine shows an accuracy of 62 .96%, which is better than the Knearest neighbour model with an accuracy of 55 . 56% . The assessment results of the pass level can be accurately predicted, which indicates that the machine learning models are able to screen the interpretations that pass the exam. The study is the first to build supervised machine learning models integrating both delivery and fidelity features to predict quality of interpreting. The machine learning models point to the great potential of automatic scoring with little human evaluation involved in the process. Automatic assessment of communication is expected to complete multitasks within a brief period by taking both holistic and analytical approaches to assess accuracy, fidelity and delivery. The proposed automatic scoring system might facilitate human-machine collaboration in the future. It can generate instant feedback for students by evaluating input renditions or abridge the workload for educators in interpreting education by screening performance for subsequent human scoring.  \nKEYWORDS  \nautomatic assessment, communication in interpreting, machine learning, computational features for fidelity, computational metrics for delivery  \n1. Introduction  \nWhe","cbCaie5nj89Lphrf","https://ap.wps.com/l/cbCaie5nj89Lphrf","pdf",866380,1,11,"English","en",105,"# Introduction\n## Background and communication models\n## Gap in prior automatic scoring research\n# Method\n## Feature extraction for delivery and fidelity\n## Machine-learning models\n# Results\n## Model comparison and accuracy\n## Pass-level prediction\n# Conclusion","[{\"question\":\"What does the study aim to achieve in interpreting assessment?\",\"answer\":\"It proposes and tests a machine-learning approach to automatically assess communication in English/Chinese interpreting, combining delivery features with information assessment.\"},{\"question\":\"Which machine-learning algorithms are used and how are they evaluated?\",\"answer\":\"The study employs K-nearest neighbour and support vector machine, comparing their performance to determine the most accurate predictive model.\"},{\"question\":\"What key finding shows the benefit of the best model?\",\"answer\":\"Support vector machine using all features achieves 62.96% accuracy and can accurately predict pass levels, indicating effectiveness for screening interpretations with less human evaluation.\"}]","Machine-learning based automatic assessment of communication in interpreting | 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does the study aim to achieve in interpreting assessment?","Question",{"text":75,"@type":76},"It proposes and tests a machine-learning approach to automatically assess communication in English/Chinese interpreting, combining delivery features with information assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine-learning algorithms are used and how are they evaluated?",{"text":80,"@type":76},"The study employs K-nearest neighbour and support vector machine, comparing their performance to determine the most accurate predictive model.",{"name":82,"@type":73,"acceptedAnswer":83},"What key finding shows the benefit of the best model?",{"text":84,"@type":76},"Support vector machine using all features achieves 62.96% accuracy and can accurately predict pass levels, indicating effectiveness for screening interpretations with less human 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