{"id":"W3100949457","doi":"10.18653/v1/2020.findings-emnlp.127","title":"Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Filter (signal processing); Context (archaeology); Knowledge extraction; Conversation; Artificial intelligence; Process (computing); Task (project management); Persona; Information retrieval; Selection (genetic algorithm); Knowledge base; Natural language processing; Machine learning; Human–computer interaction; Computer vision; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005535897,0.0001335048,0.0001624217,0.0001397309,0.00009610968,0.000147939,0.0003792981,0.0000677479,0.00001083381],"category_scores_gemma":[0.0003563331,0.0001295163,0.00005520154,0.0005557854,0.000009405553,0.0004964889,0.0001182448,0.0001329063,0.00001082193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001538749,"about_ca_system_score_gemma":0.0001349197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001200269,"about_ca_topic_score_gemma":0.00007077133,"domain_scores_codex":[0.9987238,0.0001169394,0.0002755348,0.0004800475,0.0001307024,0.0002729738],"domain_scores_gemma":[0.9993923,0.0002038798,0.00005472857,0.0001691164,0.00008853369,0.00009143808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008146101,0.0004737563,0.004294953,0.0009330587,0.0001009396,0.00007125634,0.07651357,0.06729619,0.5905958,0.1049916,0.001143617,0.1454391],"study_design_scores_gemma":[0.0007564878,0.0002584762,0.001886149,0.00004289667,0.000001575548,0.000001866034,0.00003604241,0.9689607,0.026177,0.0005157789,0.001218964,0.0001440782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.224036,0.000016011,0.7726265,0.002453187,0.00009310893,0.0002560308,7.559018e-7,0.0002102565,0.0003082085],"genre_scores_gemma":[0.8626024,3.011209e-7,0.1364694,0.0005358451,0.00008895417,0.00002200798,0.000001084246,0.00001120681,0.0002687881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9016645,"threshold_uncertainty_score":0.528152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07232172618019449,"score_gpt":0.3008768054510579,"score_spread":0.2285550792708634,"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."}}