{"id":"W2788343755","doi":"10.1609/aaai.v32i1.11273","title":"CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Recurrent neural network; Computer science; Sentence; Benchmark (surveying); Context (archaeology); Artificial intelligence; Paraphrase; Natural language processing; Word (group theory); Similarity (geometry); Language model; Artificial neural network; Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008724623,0.001007733,0.0007083397,0.0008533014,0.0003445368,0.0006159801,0.001973992,0.0009441165,0.001768907],"category_scores_gemma":[0.003226305,0.0003781302,0.0008209418,0.0008442702,0.0003280387,0.001489149,0.0006502619,0.00119536,0.0007821214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008752813,"about_ca_system_score_gemma":0.001019155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0181623,"about_ca_topic_score_gemma":0.02204239,"domain_scores_codex":[0.9995275,0.000117905,0.00003262006,0.0001972682,0.00008033802,0.0000444103],"domain_scores_gemma":[0.999371,0.0002432004,0.00008353847,0.00006729746,0.0002065778,0.00002843999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004352967,0.0002191638,0.003899921,0.0002632147,0.000345704,0.0003440327,0.0002387915,0.5238455,0.02293888,0.009333345,0.008954878,0.4291813],"study_design_scores_gemma":[0.000005688697,0.00002356897,0.00022582,0.000005755078,0.00001748358,0.00001820142,0.000004940762,0.9964006,0.001009637,0.001769296,0.0005127093,0.000006371658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07135659,0.002595035,0.9156386,0.0003810367,0.0003360923,0.0002089166,0.0009685861,0.004853109,0.003662037],"genre_scores_gemma":[0.7229157,0.001013177,0.2662592,0.0004554415,0.0002086026,0.000388751,0.002388559,0.0003848445,0.005985709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0181623,"threshold_uncertainty_score":0.0361132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1788702740475964,"score_gpt":0.3348507905021531,"score_spread":0.1559805164545567,"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."}}