Neural table-to-text generation approaches are data-hungry, limiting their
adaptation for low-resource real-world applications. Previous works mostly
resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a
table. However, they often contain hallucinated contents due to the
uncontrolled nature of PLMs. Moreover, the topological differences between
tables and sequences are rarely studied. Last but not least, fine-tuning on
PLMs with a handful of instances may lead to over-fitting and catastrophic
forgetting. To alleviate these problems, we propose a prompt-based approach,
Prefix-Controlled Generator (i.e., PCG), for few-shot table-to-text generation.
We prepend a task-specific prefix for a PLM to make the table structure better
fit the pre-trained input. In addition, we generate an input-specific prefix to
control the factual contents and word order of the generated text. Both
automatic and human evaluations on different domains (humans, books and songs)
of the Wikibio dataset show substantial improvements over baseline approaches.

本文提出了一种前缀控制生成器 (Prefix-Controlled Generator) 的方法，应用于神经表格文本生成中的少样本学习问题，该方法使用预训练语言模型，并在输入处加入特定前缀以更好地适应表格结构，同时通过输入特定前缀控制生成的文本的内容和单词顺序，基于 Wikibio 数据集的人类、书籍和歌曲等不同领域的自动和人工评估结果均表明，该方法相对于基线方法取得了实质性的改进。

控制前缀的少样本表格转文本生成器

Few-Shot Table-to-Text Generation with Prefix-Controlled Generator

Neural table-to-text generation models have achieved remarkable progress on
an array of tasks. However, due to the data-hungry nature of neural models,
their performances strongly rely on large-scale training examples, limiting
their applicability in real-world applications. To address this, we propose a
new framework: Prototype-to-Generate (P2G), for table-to-text generation under
the few-shot scenario. The proposed framework utilizes the retrieved
prototypes, which are jointly selected by an IR system and a novel prototype
selector to help the model bridging the structural gap between tables and
texts. Experimental results on three benchmark datasets with three
state-of-the-art models demonstrate that the proposed framework significantly
improves the model performance across various evaluation metrics.

提出了新的框架：Prototype-to-Generate（P2G），旨在通过使用检索的原型来提高神经表生成文本模型在少样本场景下的性能，并利用 IR 系统和新型原型选择器共同选择原型以帮助模型弥合表格和文本之间的结构差距。在三个基准数据集上进行的实验结果表明，该文所提出的框架显著提高了各种评估指标的模型性能。

原型记忆的少样本表格到文本生成

Few-Shot Table-to-Text Generation with Prototype Memory

A number of researchers have recently questioned the necessity of
increasingly complex neural network (NN) architectures. In particular, several
recent papers have shown that simpler, properly tuned models are at least
competitive across several NLP tasks. In this work, we show that this is also
the case for text generation from structured and unstructured data. We consider
neural table-to-text generation and neural question generation (NQG) tasks for
text generation from structured and unstructured data, respectively.
Table-to-text generation aims to generate a description based on a given table,
and NQG is the task of generating a question from a given passage where the
generated question can be answered by a certain sub-span of the passage using
NN models. Experimental results demonstrate that a basic attention-based
seq2seq model trained with the exponential moving average technique achieves
the state of the art in both tasks. Code is available at
this https URL

研究表明，调整得当的基本注意力 seq2seq 模型在神经网络架构的简化、文本生成、NLP 任务、以及神经表格到文本生成和神经问题生成任务中均有着最先进的表现。