We propose Future Discriminators for Generation (FUDGE), a flexible and
modular method for controlled text generation. Given a pre-existing model G for
generating text from a distribution of interest, FUDGE enables conditioning on
a desired attribute a (for example, formality) while requiring access only to
G's output logits. FUDGE learns an attribute predictor operating on a partial
sequence, and uses this predictor's outputs to adjust G's original
probabilities. We show that FUDGE models terms corresponding to a Bayesian
decomposition of the conditional distribution of G given attribute a. Moreover,
FUDGE can easily compose predictors for multiple desired attributes. We
evaluate FUDGE on three tasks -- couplet completion in poetry, topic control in
language generation, and formality change in machine translation -- and observe
gains in all three tasks.

FUDGE 是一种灵活的模块化方法，用于受控文本生成。通过使用一个属性预测器由 FUDGE 对生成的文本进行调整，FUDGE 能够实现对生成的文本的属性控制。我们评估了 FUDGE 在三种任务中的性能表现，取得了提升。

FUDGE：带有未来鉴别器的控制文本生成

FUDGE: Controlled Text Generation With Future Discriminators

We present a novel attribute learning framework named Hypergraph-based
Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the
attribute relations in the data. Then the attribute prediction problem is
casted as a regularized hypergraph cut problem in which HAP jointly learns a
collection of attribute projections from the feature space to a hypergraph
embedding space aligned with the attribute space. The learned projections
directly act as attribute classifiers (linear and kernelized). This formulation
leads to a very efficient approach. By considering our model as a multi-graph
cut task, our framework can flexibly incorporate other available information,
in particular class label. We apply our approach to attribute prediction,
Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB
databases demonstrate the value of our methods in comparison with the
state-of-the-art approaches.

该研究提出了一种基于超图的属性学习框架 ——Hypergraph-based Attribute Predictor（HAP）。通过将超图用于描述数据中的属性关系，其将属性预测问题转化为带有正则化的超图切割问题，可以应用于属性预测、零样本和 $N$-shot 学习任务中，并在 AWA、USAA 和 CUB 数据库上展现了比同类方法更具优越性的结果。