Controlled Text Generation as Continuous Optimization with Multiple Constraints
Controlled Text Generation as Continuous Optimization with Multiple Constraints
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发表时间：
2021-08
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通讯作者：
Sachin Kumar;Eric Malmi;Aliaksei Severyn;Yulia Tsvetkov
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作者：
Sachin Kumar;Eric Malmi;Aliaksei Severyn;Yulia Tsvetkov
随着大规模语言模型预训练推动了文本生成的最新技术，最近的工作转向了控制这些模型生成的文本的属性。虽然通过微调修改预训练模型仍然是流行的方法，但由于缺乏适当的数据，它会产生大量的计算成本，并且可能是不可行的。作为替代方案，我们提出了MuCoCO——一种灵活的模块化算法，用于从预训练模型进行可控推断。我们将解码过程表述为一个优化问题，该问题允许我们目标控制的多个属性容易地作为优化的可微分约束合并。通过将这种离散优化放松为连续优化，我们使用拉格朗日乘法器和基于梯度下降的技术来生成所需的文本。我们在多句子级属性的可控机器翻译和风格迁移上评估了我们的方法，并观察到在基线上的显著改进。
As large-scale language model pretraining pushes the state-of-the-art in text generation, recent work has turned to controlling attributes of the text such models generate. While modifying the pretrained models via fine-tuning remains the popular approach, it incurs a significant computational cost and can be infeasible due to lack of appropriate data. As an alternative, we propose MuCoCO -- a flexible and modular algorithm for controllable inference from pretrained models. We formulate the decoding process as an optimization problem which allows for multiple attributes we aim to control to be easily incorporated as differentiable constraints to the optimization. By relaxing this discrete optimization to a continuous one, we make use of Lagrangian multipliers and gradient-descent based techniques to generate the desired text. We evaluate our approach on controllable machine translation and style transfer with multiple sentence-level attributes and observe significant improvements over baselines.