Pre-trained language models (PLMs) have made remarkable progress in
table-to-text generation tasks. However, the topological gap between tabular
data and text and the lack of domain-specific knowledge make it difficult for
PLMs to produce faithful text, especially in real-world applications with
limited resources. In this paper, we mitigate the above challenges by
introducing a novel augmentation method: Prompt-based Adapter (PA), which
targets table-to-text generation under few-shot conditions. The core insight
design of the PA is to inject prompt templates for augmenting domain-specific
knowledge and table-related representations into the model for bridging the
structural gap between tabular data and descriptions through adapters. Such
prompt-based knowledge augmentation method brings at least two benefits: (1)
enables us to fully use the large amounts of unlabelled domain-specific
knowledge, which can alleviate the PLMs' inherent shortcomings of lacking
domain knowledge; (2) allows us to design different types of tasks supporting
the generative challenge. Extensive experiments and analyses are conducted on
three open-domain few-shot NLG datasets: Humans, Books, and Songs. Compared to
previous state-of-the-art approaches, our model achieves superior performance
in terms of both fluency and accuracy as judged by human and automatic
evaluations.

本文介绍了一种基于 Prompt-based Adapter (PA) 的方法，通过注入 prompt 模板对特定领域的知识和表格相关的表示进行数据增强，从而在少量样本情况下实现表格到文本的生成任务。相比之前的方法，在 Humans、Books 和 Songs 等三个数据集上，此方法表现更好。

基于提示的少样本表格文本生成适配器

Few-Shot Table-to-Text Generation with Prompt-based Adapter

One of the biggest challenges of natural language generation (NLG) is the
proper handling of named entities. Named entities are a common source of
grammar mistakes such as wrong prepositions, wrong article handling, or
incorrect entity inflection. Without factoring linguistic representation, such
errors are often underrepresented when evaluating on a small set of arbitrarily
picked argument values, or when translating a dataset from a linguistically
simpler language, like English, to a linguistically complex language, like
Russian. However, for some applications, broadly precise grammatical
correctness is critical -- native speakers may find entity-related grammar
errors silly, jarring, or even offensive.
To enable the creation of more linguistically diverse NLG datasets, we
release a Corpus of Linguistically Significant Entities (CLSE) annotated by
linguist experts. The corpus includes 34 languages and covers 74 different
semantic types to support various applications from airline ticketing to video
games. To demonstrate one possible use of CLSE, we produce an augmented version
of the Schema-Guided Dialog Dataset, SGD-CLSE. Using the CLSE's entities and a
small number of human translations, we create a linguistically representative
NLG evaluation benchmark in three languages: French (high-resource), Marathi
(low-resource), and Russian (highly inflected language). We establish quality
baselines for neural, template-based, and hybrid NLG systems and discuss the
strengths and weaknesses of each approach.

本文介绍了如何处理元素实体以创建更具语言多样性的自然语言生成数据集，并构建了一个基于此数据集的评估基准，用于评估神经网络，模板和混合 NLG 系统的优缺点。

CLSE: 语言学意义实体语料库

CLSE: Corpus of Linguistically Significant Entities

Neural natural language generation (NNLG) from structured meaning
representations has become increasingly popular in recent years. While we have
seen progress with generating syntactically correct utterances that preserve
semantics, various shortcomings of NNLG systems are clear: new tasks require
new training data which is not available or straightforward to acquire, and
model outputs are simple and may be dull and repetitive. This paper addresses
these two critical challenges in NNLG by: (1) scalably (and at no cost)
creating training datasets of parallel meaning representations and reference
texts with rich style markup by using data from freely available and naturally
descriptive user reviews, and (2) systematically exploring how the style markup
enables joint control of semantic and stylistic aspects of neural model output.
We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations
and highly stylistically varied reference texts spanning different restaurant
attributes, and describe a novel methodology that can be scalably reused to
generate NLG datasets for other domains. The experiments show that the models
control important aspects, including lexical choice of adjectives, output
length, and sentiment, allowing the models to successfully hit multiple style
targets without sacrificing semantics.

通过自然描述性用户评论数据免费生成语义和样式标记富文本的可并行结构化意义表示的训练数据集，系统地探索样式标记如何实现神经模型输出的语义和样式联合控制，提出了 YelpNLG，一个跨越不同餐厅属性的高度风格多变的参考文本和富含语义的并行结构化意义表示语料库，实验结果表明该模型可以在保持语义的前提下，成功地达到多个样式目标，包括形容词的词汇选择，输出长度和情感。