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Question Answering as Global Reasoning Over Semantic Abstractions

AAAI 2018 · Large language models › Retrieval and knowledge · approach: Explanatory and multiple-choice QA benchmarks · 56 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SemanticILP instead of TupleInfmultiple-choice science QA · Regents 4thtest score (%): 67.6 vs. 61.4Helped
Use SemanticILP instead of BiDAF'multiple-choice science QA · AI2Public 4thtest score (%): 59.7 vs. 57.4Helped
Use SemanticILP instead of IRmultiple-choice science QA · Regents 8thtest score (%): 66.0 vs. 64.2Helped
Use SemanticILP instead of IRmultiple-choice science QA · AI2Public 8thtest score (%): 55.9 vs. 52.8Helped
Use SemanticILP instead of Proreadbiology reading comprehension · ProcessBanktest score (%): 67.9 vs. 68.1Hurt
Add Comb-1 (shallow alignment) (ablation)SemanticILP · AI2Public 8thtest score: -3.1 when removedHelped
Add Comb-1 (shallow alignment) (ablation)SemanticILP · ProcessBanktest score: -1.8 when removedHelped
Add Comb-2 (Verb-SRL alignment) (ablation)SemanticILP · AI2Public 8thtest score: -2.0 when removedHelped
Add Comb-2 (Verb-SRL alignment) (ablation)SemanticILP · ProcessBanktest score: -4.6 when removedHelped
Add Comb-3 (Verb-SRL and coreference alignment) (ablation)SemanticILP · AI2Public 8thtest score: -0.6 when removedHelped
Add Comb-3 (Verb-SRL and coreference alignment) (ablation)SemanticILP · ProcessBanktest score: -1.8 when removedHelped
Add Comb-4 (Verb-SRL and Comma-SRL alignment) (ablation)SemanticILP · AI2Public 8thtest score: -3.1 when removedHelped

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