{"id":"W3198711991","doi":"10.1162/tacl_a_00478","title":"It’s not Rocket Science: Interpreting Figurative Language in Narratives","year":2022,"lang":"en","type":"article","venue":"Transactions of the Association for Computational Linguistics","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Literal and figurative language; Computer science; Natural language processing; Principle of compositionality; Narrative; Generative grammar; Interpretation (philosophy); Artificial intelligence; Linguistics; Context (archaeology); Expression (computer science); Programming language; History","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":[0.001988821,0.0009794856,0.0002355631,0.001015928,0.000521685,0.00265385,0.001045999,0.001309478,0.003542212],"category_scores_gemma":[0.01511593,0.0003454076,0.0007225164,0.0006158512,0.001275472,0.004880017,0.001058235,0.001432217,0.001306471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142225,"about_ca_system_score_gemma":0.0006394879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004974572,"about_ca_topic_score_gemma":0.008166929,"domain_scores_codex":[0.9988996,0.0007053089,0.00004643949,0.0002270734,0.00007719911,0.00004442382],"domain_scores_gemma":[0.9926323,0.005790607,0.0004418458,0.0007236254,0.0002859941,0.000125611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001762758,0.0006217144,0.09854395,0.003207957,0.0004323576,0.004328845,0.05103011,0.1519861,0.03640646,0.06904928,0.0457736,0.5368568],"study_design_scores_gemma":[0.0001005287,0.0002337553,0.02136479,0.0007934897,0.0001329282,0.002029757,0.01119916,0.7836898,0.02977175,0.06796018,0.08251569,0.0002080661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7994957,0.002372486,0.1623082,0.00400672,0.0002805963,0.0003472951,0.007301,0.004466206,0.01942169],"genre_scores_gemma":[0.9297994,0.0003717009,0.06238274,0.0002950877,0.00003712642,0.00006407498,0.005153553,0.0001575418,0.001738876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004974572,"threshold_uncertainty_score":0.01184988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01209388290123788,"score_gpt":0.3034295858987213,"score_spread":0.2913357029974835,"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."}}