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The ability to generate natural-language questions with controlled complexitylevels is highly desirable as it further expands the applicability of questiongeneration. In this paper, we propose an end-to-end neuralcomplexity-controllable question generation model, which incorporates a mixtureof experts (MoE) as the selector of soft templates to improve the accuracy ofcomplexity control and the quality of generated questions. The soft templatescapture question similarity while avoiding the expensive construction of actualtemplates. Our method introduces a novel, cross-domain complexity estimator toassess the complexity of a question, taking into account the passage, thequestion, the answer and their interactions. The experimental results on twobenchmark QA datasets demonstrate that our QG model is superior tostate-of-the-art methods in both automatic and manual evaluation. Moreover, ourcomplexity estimator is significantly more accurate than the baselines in bothin-domain and out-domain settings.

Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model