{"id":"W2132959801","doi":"","title":"Incremental Segmentation and Decoding Strategies for Simultaneous Translation","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Natural Language Processing","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Decoding methods; Segmentation; Artificial intelligence; Machine translation; Speech recognition; Interpreter; Natural language processing; Speech translation; Active listening; Phrase; Task (project management); Latency (audio); Translation (biology); Algorithm; Programming language; Communication","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":[0.00118976,0.00154588,0.0007749198,0.0009082578,0.0006734453,0.001715172,0.001598369,0.001723683,0.00477539],"category_scores_gemma":[0.007382683,0.0005481722,0.0007531188,0.001251815,0.0008316542,0.002230992,0.001337794,0.00155192,0.003442335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005516809,"about_ca_system_score_gemma":0.001468561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00271028,"about_ca_topic_score_gemma":0.005075884,"domain_scores_codex":[0.9985215,0.0004960381,0.0001119221,0.0003231731,0.0004087095,0.0001386537],"domain_scores_gemma":[0.9955238,0.002467539,0.0001526054,0.0007412669,0.001000225,0.0001145576],"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":[0.0009165783,0.0001823102,0.001149922,0.0004554378,0.00007999012,0.0004687773,0.001352361,0.03824735,0.1724898,0.01812171,0.003840883,0.7626949],"study_design_scores_gemma":[0.0001429355,0.0005070817,0.001483224,0.00005686787,0.0001889958,0.001391553,0.0005531037,0.6521763,0.3029024,0.02456017,0.0158797,0.000157739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0240944,0.0003484297,0.9680388,0.0001061177,0.00006866621,0.00009135614,0.0001251158,0.003611633,0.003515601],"genre_scores_gemma":[0.2474658,0.0003203956,0.7465414,0.0001426069,0.00006703437,0.0001823087,0.0006952788,0.001048253,0.003536893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00477539,"threshold_uncertainty_score":0.01597524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02833968079378524,"score_gpt":0.3111720211243554,"score_spread":0.2828323403305702,"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."}}