{"id":"W2786983967","doi":"10.24963/ijcai.2018/609","title":"An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Conversation; Ranking (information retrieval); Natural language generation; Generative grammar; Margin (machine learning); Utterance; Generator (circuit theory); Artificial intelligence; Process (computing); Information retrieval; Natural language processing; Artificial neural network; Natural language; Machine learning; 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":[0.002805396,0.001270859,0.001462742,0.001337921,0.001126162,0.001226422,0.001586033,0.001338418,0.002017026],"category_scores_gemma":[0.004415407,0.0006204631,0.001111323,0.0009298092,0.0004688337,0.002878696,0.002197324,0.0012674,0.001494692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005832874,"about_ca_system_score_gemma":0.001159905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004883686,"about_ca_topic_score_gemma":0.005115542,"domain_scores_codex":[0.998556,0.0003883099,0.00008571724,0.0005214757,0.000309233,0.0001392638],"domain_scores_gemma":[0.9983498,0.0005055644,0.00009628451,0.0003064415,0.0005602127,0.0001817631],"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":[0.0007526788,0.0005894863,0.01059383,0.000342388,0.0005862039,0.0005141799,0.001166126,0.1071805,0.05389344,0.005638683,0.008327752,0.8104147],"study_design_scores_gemma":[0.00002273955,0.0002081838,0.002002581,0.00001847504,0.0001946163,0.0002784335,0.000105499,0.9795765,0.008769693,0.003673777,0.005099832,0.00004965103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09013939,0.002025007,0.8931298,0.0006658401,0.0002991331,0.000364149,0.0002279094,0.007617002,0.00553176],"genre_scores_gemma":[0.72307,0.0006695226,0.2663662,0.0003837371,0.0003585847,0.0002806078,0.0008157353,0.0002561704,0.007799387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004883686,"threshold_uncertainty_score":0.01483655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03805082076957427,"score_gpt":0.2712421793445915,"score_spread":0.2331913585750173,"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."}}