{"id":"W3109558947","doi":"10.1609/aaai.v35i14.17527","title":"DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Computer science; Utterance; Security token; Transformer; Natural language processing; Coherence (philosophical gambling strategy); Artificial intelligence; Conversation; Speech recognition; Language model; Psychology; Communication","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006460089,0.0001696952,0.0002088972,0.0000888553,0.0002372997,0.0003671762,0.0007297792,0.00006703714,0.00004484967],"category_scores_gemma":[0.0006798594,0.0001373838,0.00006195616,0.0004288575,0.00009275231,0.0004469828,0.0003731763,0.0002455341,0.00003097588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003455117,"about_ca_system_score_gemma":0.0001009563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002917621,"about_ca_topic_score_gemma":0.0000268664,"domain_scores_codex":[0.9983643,0.00006209838,0.0003855473,0.0005701887,0.0003582616,0.0002596513],"domain_scores_gemma":[0.9988793,0.0001207168,0.0001745839,0.0002471207,0.0004853724,0.00009290197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002036951,0.00008839821,0.0006042843,0.00003544375,0.00001821369,0.000003225372,0.007337601,0.002272465,0.4901787,0.1301381,0.00010048,0.3690193],"study_design_scores_gemma":[0.00003176892,0.0001383556,0.0003331461,0.000140162,0.000008299526,0.00001120837,0.0005567278,0.3653117,0.6130599,0.01995829,0.0002236033,0.0002267828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6692301,0.00002813589,0.3180653,0.01132404,0.0004630028,0.0002014264,0.000001478533,0.00005250258,0.0006339701],"genre_scores_gemma":[0.993459,0.00003359957,0.005478972,0.0004585783,0.0001162259,0.00001969118,4.430841e-7,0.000009250608,0.0004242475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3687925,"threshold_uncertainty_score":0.5602349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07664952376012893,"score_gpt":0.3068271529254254,"score_spread":0.2301776291652965,"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."}}