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Learning From Unannotated QA Pairs to Analogically Disambiguate and Answer Questions

AAAI 2018 · Large language models › Retrieval and knowledge · approach: Explanatory and multiple-choice QA benchmarks · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
correct the 22 Geoquery gold queries that evaluate incorrectlyOur System, 10-fold · Geoqueryanswer accuracy (%): 86.3 -> 88.8Helped
correct the 22 Geoquery gold queries that evaluate incorrectlyOur System, 2-fold · Geoqueryanswer accuracy (%): 84.8 -> 87.4Helped
reduce cross-validation from 10-fold to 2-fold (far fewer training examples)Our System · Geoquery correctedanswer accuracy (%): 88.8 -> 87.4Hurt
reduce training set to 10 QA pairsOur System · Geoquery correctedanswer accuracy (%): 88.8 -> 51.6Hurt
use only 100 QA pairs for training instead of full 10-fold trainingOur System · Geoquery correctedanswer accuracy (%): 88.8 -> 85.6No clear effect
compare unannotated-QA training against annotated logical-form training (KZGS10)Our System corrected 10-fold · Geoqueryanswer accuracy (%): 88.9 -> 88.8No clear effect
LJK11 manually augmented triggers versus base triggersLJK11, 680/200 · Geoqueryanswer accuracy (%): 87.9 -> 91.4Helped

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