{"id":"W2963412005","doi":"10.18653/v1/d16-1233","title":"Conditional Generation and Snapshot Learning in Neural Dialogue Systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Institute for Catastrophic Loss Reduction","keywords":"Snapshot (computer storage); Computer science; Artificial intelligence; Artificial neural network; Natural language processing; Cognitive science; Machine learning; Psychology; Operating system","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003974202,0.0006634598,0.001287992,0.0008274071,0.0007012218,0.00151016,0.001988046,0.001338292,0.003994774],"category_scores_gemma":[0.02116398,0.0008157483,0.0005354696,0.000690865,0.001255421,0.004276765,0.002944919,0.002338608,0.0005683884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001163465,"about_ca_system_score_gemma":0.000899228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004522767,"about_ca_topic_score_gemma":0.006643321,"domain_scores_codex":[0.9983359,0.001010557,0.00006956107,0.000332909,0.0001261805,0.0001250392],"domain_scores_gemma":[0.9826602,0.01507469,0.0003503136,0.0008615293,0.000655317,0.0003979323],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008491372,0.0001742739,0.0032279,0.0002796904,0.0001612679,0.0002163259,0.0007721269,0.6561083,0.001535987,0.1394867,0.007296574,0.1898918],"study_design_scores_gemma":[0.00001612835,0.0000179319,0.0001324248,0.00000701981,0.000007625573,0.00001610064,0.00001547848,0.9307482,0.0002796853,0.06847088,0.0002799161,0.000008608237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06529456,0.001801351,0.9274912,0.001025635,0.0001274015,0.00006465286,0.00025474,0.001401605,0.002538787],"genre_scores_gemma":[0.8870612,0.0003630525,0.1077898,0.000213934,0.0001399708,0.0001594277,0.0006357273,0.0002840513,0.003352809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004522767,"threshold_uncertainty_score":0.02101785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0388995657737141,"score_gpt":0.2375652178747476,"score_spread":0.1986656521010335,"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."}}