We present an automatic text expansion system to generate English sentences,
which performs automatic Natural Language Generation (NLG) by combining
linguistic rules with statistical approaches. Here, "automatic" means that the
system can generate coherent and correct sentences from a minimum set of words.
From its inception, the design is modular and adaptable to other languages.
This adaptability is one of its greatest advantages. For English, we have
created the highly precise aLexiE lexicon with wide coverage, which represents
a contribution on its own. We have evaluated the resulting NLG library in an
Augmentative and Alternative Communication (AAC) proof of concept, both
directly (by regenerating corpus sentences) and manually (from annotations)
using a popular corpus in the NLG field. We performed a second analysis by
comparing the quality of text expansion in English to Spanish, using an ad-hoc
Spanish-English parallel corpus. The system might also be applied to other
domains such as report and news generation.

我们提出了一个自动文本扩展系统，结合语言规则和统计方法，进行自动的自然语言生成。该系统可以从最小词汇集合生成连贯和正确的英文句子。

自动英文文本扩展系统

A System for Automatic English Text Expansion

This work presents a new task of Text Expansion (TE), which aims to insert
fine-grained modifiers into proper locations of the plain text to concretize or
vivify human writings. Different from existing insertion-based writing
assistance tasks, TE requires the model to be more flexible in both locating
and generation, and also more cautious in keeping basic semantics. We leverage
four complementary approaches to construct a dataset with 12 million
automatically generated instances and 2K human-annotated references for both
English and Chinese. To facilitate automatic evaluation, we design various
metrics from multiple perspectives. In particular, we propose Info-Gain to
effectively measure the informativeness of expansions, which is an important
quality dimension in TE. On top of a pre-trained text-infilling model, we build
both pipelined and joint Locate&Infill models, which demonstrate the
superiority over the Text2Text baselines, especially in expansion
informativeness. Experiments verify the feasibility of the TE task and point
out potential directions for future research toward better automatic text
expansion.

本文提出了文本扩展（TE）的新任务，旨在插入细粒度修改器到纯文本的适当位置以具象化或使人类写作更生动。通过四种互补方法，我们构建了一份包含 1200 万个自动生成实例和 2000 个人工注释的英文和中文数据集。为了便于自动评估，我们从多个角度设计了各种度量指标，特别是提出了 Info-Gain 来有效度量扩展的信息量，它是 TE 任务中一个重要的质量维度。在基于预训练的文本填充模型的基础上，我们构建了基于流水线和联合定位和填充模型，证明了其在扩展信息量上优于 Text2Text 基线方法。实验证实了 TE 任务的可行性，并指出了未来研究朝着更好的自动文本扩展方向的潜在研究方向。