Meta Sequence Learning and Its Applications
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We present a meta-sequence representation of sentences and demonstrate how to use meta sequence learning to generate adequate question-answer pairs (QAPs) over a given article. A meta sequence is a sequence of vectors of semantic and syntactic tags. On a given declarative sentence, a trained model converts it to a meta sequence, finds a matched meta sequence in its learned database, and uses the corresponding meta sequence for interrogative sentence to generate QAPs. We show that, trained on a small dataset, our method generates efficiently, on the official SAT practice reading tests, a large number of syntactically and semantically correct QAPs with high accuracy.
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