Multi-aspect controllable text generation is a more challenging and practical
task than single-aspect control. Existing methods achieve complex multi-aspect
control by fusing multiple controllers learned from single-aspect, but suffer
from attribute degeneration caused by the mutual interference of these
controllers. To address this, we provide observations on attribute fusion from
a distributional perspective and propose to directly search for the
intersection areas of multiple attribute distributions as their combination for
generation. Our method first estimates the attribute space with an autoencoder
structure. Afterward, we iteratively approach the intersections by jointly
minimizing distances to points representing different attributes. Finally, we
map them to attribute-relevant sentences with a prefix-tuning-based decoder.
Experiments on the three-aspect control task, including sentiment, topic, and
detoxification aspects, reveal that our method outperforms several strong
baselines on attribute relevance and text quality and achieves the SOTA.
Further analysis also supplies some explanatory support for the effectiveness
of our approach.

通过分布式的角度提供属性融合的观察，建议直接搜索作为它们的生成组合的多个属性分布的交集区域。用自编码器结构估计属性空间，通过共同最小化到表示不同属性的点的距离来迭代地接近交集，最后用基于前缀调整的解码器将其映射到与属性相关的句子中。

多角度可控文本生成的分布式视角

A Distributional Lens for Multi-Aspect Controllable Text Generation

Domain adaptive semantic segmentation attempts to make satisfactory dense
predictions on an unlabeled target domain by utilizing the supervised model
trained on a labeled source domain. In this work, we propose Semantic-Guided
Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights
the semantic concepts of individual pixels to promote learning of
class-discriminative and class-balanced pixel representations across domains,
eventually boosting the performance of self-training methods. Specifically, to
explore proper semantic concepts, we first investigate a centroid-aware pixel
contrast that employs the category centroids of the entire source domain or a
single source image to guide the learning of discriminative features.
Considering the possible lack of category diversity in semantic concepts, we
then blaze a trail of distributional perspective to involve a sufficient
quantity of instances, namely distribution-aware pixel contrast, in which we
approximate the true distribution of each semantic category from the statistics
of labeled source data. Moreover, such an optimization objective can derive a
closed-form upper bound by implicitly involving an infinite number of
(dis)similar pairs, making it computationally efficient. Extensive experiments
show that SePiCo not only helps stabilize training but also yields
discriminative representations, making significant progress on both
synthetic-to-real and daytime-to-nighttime adaptation scenarios.

该论文提出了一种新的一步自适应框架 SePiCo，它通过强调单个像素的语义概念来促进跨域自学习方法的学习，并在合成到真实和白天到夜晚自适应情景下取得了显著的进展。