{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190501962","title":"Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector","arxiv_id":"1905.01962","date":"2019-04-10","proceeding":"SEMEVAL 2019 6","authors":["Mehdi Drissi","Pedro Sandoval","Vivaswat Ojha","Julie Medero"],"abstract":"We investigate the recently developed Bidirectional Encoder Representations\nfrom Transformers (BERT) model for the hyperpartisan news detection task. Using\na subset of hand-labeled articles from SemEval as a validation set, we test the\nperformance of different parameters for BERT models. We find that accuracy from\ntwo different BERT models using different proportions of the articles is\nconsistently high, with our best-performing model on the validation set\nachieving 85% accuracy and the best-performing model on the test set achieving\n77%. We further determined that our model exhibits strong consistency, labeling\nindependent slices of the same article identically. Finally, we find that\nrandomizing the order of word pieces dramatically reduces validation accuracy\n(to approximately 60%), but that shuffling groups of four or more word pieces\nmaintains an accuracy of about 80%, indicating the model mainly gains value\nfrom local context.","url_abs":"http://arxiv.org/abs/1905.01962v1","url_pdf":"http://arxiv.org/pdf/1905.01962v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190501962","repo_url":"https://github.com/hmc-cs159-fall2018/final-project-team-mvp-10000","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}