Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
Table 4: Manual evaluation results. All metrics are rated on a 1-3 scale (3 for the best). Two-tailed t-test results are shown for our CFNet compared to PGNet/MSNet. * indicates p-value <0.01.
['[EMPTY]', 'Grammaticality', 'Answerability', 'Interconnectedness']
All models achieve high scores on “Grammaticality”, owing to the strong language modeling capability of neural models. MSNet and our CFNet perform well on “Answerability” while PGNet does not. This demonstrates our base model MSNet and our CFNet can ask questions to the point. Finally, our CFNet outperforms the other two models in terms of “Interconnectedness” by a large gap, which proves that the proposed coreference alignment and conversation flow modeling can effectively make questions conversational.