{"id":"W2894995323","doi":"10.1007/978-3-030-01716-3_32","title":"Coherence-Based Automated Essay Scoring Using Self-attention","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Feature engineering; Coherence (philosophical gambling strategy); Artificial intelligence; Representation (politics); Feature (linguistics); Machine learning; Natural language processing; Deep learning; Linguistics; Statistics","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":[0.00375505,0.001136311,0.001426342,0.003487217,0.0007327184,0.002009802,0.001408096,0.0009285229,0.007987394],"category_scores_gemma":[0.01613567,0.0003884109,0.0005152752,0.002021364,0.0002775447,0.002207391,0.002671876,0.001123243,0.006341803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003650643,"about_ca_system_score_gemma":0.0007593195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001561701,"about_ca_topic_score_gemma":0.003591793,"domain_scores_codex":[0.9953315,0.001657137,0.0003463577,0.001027746,0.00128504,0.000352266],"domain_scores_gemma":[0.983085,0.007687558,0.00106829,0.001846822,0.00561134,0.0007010039],"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":[0.0005701786,0.0003812031,0.009994492,0.0002189547,0.0001540792,0.00008090641,0.0002928989,0.004514722,0.02030077,0.000994231,0.02051525,0.9419823],"study_design_scores_gemma":[0.0001879954,0.0007723087,0.03494808,0.00006243277,0.0002425137,0.0003221968,0.0005921713,0.9125438,0.02988615,0.008741661,0.01157712,0.0001236398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2078947,0.001660503,0.7452776,0.0004697706,0.0008153691,0.0006806718,0.002264495,0.02604062,0.01489627],"genre_scores_gemma":[0.7288219,0.0002586738,0.2468517,0.0001290821,0.0005878033,0.0004162061,0.005447138,0.0008931307,0.01659427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007987394,"threshold_uncertainty_score":0.02672046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0288393412473853,"score_gpt":0.2639626134913965,"score_spread":0.2351232722440112,"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."}}