{"id":"W3117574438","doi":"10.18653/v1/2020.semeval-1.54","title":"CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs","year":2020,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Argumentative; Computer science; Task (project management); SemEval; Artificial intelligence; Natural language processing; Set (abstract data type); Word (group theory); Linguistics; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00161723,0.002641639,0.00116842,0.0006572405,0.0007426497,0.001872024,0.002659066,0.003149774,0.02275322],"category_scores_gemma":[0.00496788,0.0006211702,0.000785928,0.0005676897,0.0004891857,0.004327804,0.002210173,0.002869848,0.01172316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009364479,"about_ca_system_score_gemma":0.00134868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006459409,"about_ca_topic_score_gemma":0.01117723,"domain_scores_codex":[0.9991823,0.0001617623,0.0000369282,0.000360476,0.00012249,0.0001361229],"domain_scores_gemma":[0.9983839,0.0005058672,0.000066646,0.0004932035,0.0004039754,0.0001464376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002479254,0.001121083,0.003531729,0.001616973,0.0005207445,0.00109343,0.0005999315,0.04317635,0.1058606,0.008879457,0.2658889,0.5652314],"study_design_scores_gemma":[0.000420054,0.0009330863,0.005541534,0.0001518347,0.0001665907,0.0009207154,0.000359778,0.815906,0.09865888,0.01865199,0.05810181,0.0001877605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4327278,0.003687385,0.2937064,0.002359396,0.00369669,0.001113444,0.0527023,0.1484834,0.06152332],"genre_scores_gemma":[0.7063289,0.0003256181,0.2025717,0.001126085,0.0003271826,0.0007885376,0.06025469,0.003089169,0.02518812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02275322,"threshold_uncertainty_score":0.07611704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503042012673251,"score_gpt":0.2599533378869339,"score_spread":0.2449229177602014,"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."}}