{"id":"W2953591948","doi":"10.18653/v1/s19-2188","title":"UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs","year":2019,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"SemEval; Computer science; Task (project management); Ranking (information retrieval); Artificial intelligence; Natural language processing; Competition (biology); Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006251506,0.004555582,0.002157474,0.002442315,0.001944851,0.00373355,0.003031769,0.005074129,0.02694299],"category_scores_gemma":[0.01393175,0.001048371,0.001794736,0.002306691,0.0008125679,0.004729112,0.004010722,0.004843539,0.03135184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001845615,"about_ca_system_score_gemma":0.002519982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01712045,"about_ca_topic_score_gemma":0.02386935,"domain_scores_codex":[0.9955868,0.001249207,0.0002212807,0.001492452,0.0008553637,0.0005950438],"domain_scores_gemma":[0.99391,0.001837134,0.0001922831,0.001442533,0.002040934,0.0005772055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001608132,0.0009255167,0.00334787,0.001008583,0.0004531878,0.0007293606,0.0003367549,0.01959186,0.01504583,0.001899617,0.6832392,0.2718141],"study_design_scores_gemma":[0.001420893,0.001276145,0.008904081,0.0003420719,0.0003592792,0.001190517,0.0009464321,0.6616378,0.06768806,0.01130609,0.2446326,0.0002959042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.2910225,0.01099001,0.1751092,0.01028714,0.0164803,0.002616562,0.2030359,0.2114377,0.07902088],"genre_scores_gemma":[0.3343369,0.001120842,0.1839332,0.002551558,0.00127064,0.001610004,0.3994759,0.007857332,0.06784365],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02694299,"threshold_uncertainty_score":0.09013331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01075782620552996,"score_gpt":0.2112432914759302,"score_spread":0.2004854652704003,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}