{"id":"W4403582839","doi":"10.1145/3627673.3679534","title":"Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search","year":2024,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Beijing Jiaotong University","keywords":"Computer science; Query expansion; Relevance (law); Sargable; Information retrieval; Query optimization; Query language; Web search query; Representation (politics); Relevance feedback; Web query classification; RDF query language; Query by Example; Search engine; Natural language processing; Artificial intelligence; Image retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766777,0.00005940924,0.00006487037,0.0001154937,0.00003163042,0.0001619924,0.0001328958,0.00002584418,0.000009298909],"category_scores_gemma":[0.00002227212,0.00005071851,0.000009252507,0.0002536428,0.00002209651,0.0006872247,0.00009877922,0.0001021558,0.00001140525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004909262,"about_ca_system_score_gemma":0.0000746009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003740815,"about_ca_topic_score_gemma":0.00006270014,"domain_scores_codex":[0.9990747,0.00004095541,0.0001321608,0.0003678288,0.0002506943,0.0001336866],"domain_scores_gemma":[0.9995596,0.0001780434,0.00001240753,0.0001859228,0.00002977973,0.00003427852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005035231,0.00007287971,0.144456,0.0003298499,0.00009152517,0.0005533344,0.01399752,0.01836115,0.004894656,0.5896397,0.00227137,0.2252817],"study_design_scores_gemma":[0.0002477544,0.00002512714,0.006858986,0.0001492583,0.000001943176,0.00002823525,0.000218311,0.9867219,0.001512848,0.003796198,0.0003227745,0.000116695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2378544,0.0002135261,0.7546282,0.003264891,0.0001300427,0.000131091,4.371988e-7,0.0001359087,0.003641445],"genre_scores_gemma":[0.9288306,0.00003300743,0.07004192,0.0002299465,0.00003324784,0.000009639106,0.000001922078,0.000004899263,0.0008148598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9683607,"threshold_uncertainty_score":0.2068241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03131823917913042,"score_gpt":0.2876436426709154,"score_spread":0.256325403491785,"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."}}