Papers with MixQG

1 papers
MixQG: Neural Question Generation with Mixed Answer Types (2022.findings-naacl)

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Challenge: Existing neural question generation approaches focus on short factoid type of answers.
Approach: They propose a neural question generator that trains a single generative model by combining multiple question types with different answer types.
Outcome: The proposed model outperforms existing models in both seen and unseen domains and can generate questions with different cognitive levels when conditioned on different answer types.

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