  Controlling output length in neural language generation is valuable in many
scenarios, especially for the tasks that have length constraints. A model with
stronger length control capacity can produce sentences with more specific
length, however, it usually sacrifices semantic accuracy of the generated
sentences. Here, we denote a concept of Controllable Length Control (CLC) for
the trade-off between length control capacity and semantic accuracy of the
language generation model. More specifically, CLC is to alter length control
capacity of the model so as to generate sentence with corresponding quality.
This is meaningful in real applications when length control capacity and
outputs quality are requested with different priorities, or to overcome
unstability of length control during model training. In this paper, we propose
two reinforcement learning (RL) methods to adjust the trade-off between length
control capacity and semantic accuracy of length control models. Results show
that our RL methods improve scores across a wide range of target lengths and
achieve the goal of CLC. Additionally, two models LenMC and LenLInit modified
on previous length-control models are proposed to obtain better performance in
summarization task while still maintain the ability to control length.
