Existing data-to-text generation efforts mainly focus on generating a
coherent text from non-linguistic input data, such as tables and
attribute-value pairs, but overlook that different application scenarios may
require texts of different styles. Inspired by this, we define a new task,
namely stylized data-to-text generation, whose aim is to generate coherent text
for the given non-linguistic data according to a specific style. This task is
non-trivial, due to three challenges: the logic of the generated text,
unstructured style reference, and biased training samples. To address these
challenges, we propose a novel stylized data-to-text generation model, named
StyleD2T, comprising three components: logic planning-enhanced data embedding,
mask-based style embedding, and unbiased stylized text generation. In the first
component, we introduce a graph-guided logic planner for attribute organization
to ensure the logic of generated text. In the second component, we devise
feature-level mask-based style embedding to extract the essential style signal
from the given unstructured style reference. In the last one, pseudo triplet
augmentation is utilized to achieve unbiased text generation, and a
multi-condition based confidence assignment function is designed to ensure the
quality of pseudo samples. Extensive experiments on a newly collected dataset
from Taobao have been conducted, and the results show the superiority of our
model over existing methods.

提出了一个新的任务 —— 风格化数据到文本生成，旨在根据特定的风格为给定的非语言数据生成连贯的文本。为应对逻辑、风格和偏差等三个挑战，我们提出了一个名为 StyleD2T 的新型模型，包括三个组件：逻辑规划增强数据嵌入、基于掩码的风格嵌入和无偏向的风格化文本生成。