  Question generation is a conditioned language generation task that consists
in generating a context-aware question given a context and the targeted answer.
Train language modelling with a mere likelihood maximization has been widely
used while suffering from exposure bias and the discordance between the
training and the test metrics. In the way of addressing this issue, The
presented work portrays a fully Transformer-based reinforcement learning
generator-evaluation architecture for neural question generation. To edge the
flexibility of the generation, a semantic-based reward score was externally
infused during the training to drive the training of the language model. The
global architecture is laid out in a generator-evaluator fashion optimized
directly to n-gram and semantic-based metrics. Evaluation metrics for language
modelling only based on n-gram overlapping do not consider semantic relations
between reference and candidate sequences. To improve the evaluation step, a
two-fold evaluation was carried out. On the one side, an n-gram overlapping
evaluation using the BLEU score. On the other side, a semantic-based assessment
using BERTScore and NUBIA. The results were corroborated by a binary human
evaluation of the semantic relatedness of the generated question and the ground
truth. The results obtained showed that use a semantic-based REINFORCE
algorithm for the question generation syntactically reshapes the generated
questions while preserving their underlying semantic meaning. Many downstream
applications can be drawn from a successful question generation including the
enlargement of question answering datasets, the improvement of conversational
systems, the enhancement of autonomous educational assessment systems, and so
forth.
