We support scientific writers in determining whether a written sentence is
scientific, to which section it belongs, and suggest paraphrasings to improve
the sentence. Firstly, we propose a regression model trained on a corpus of
scientific sentences extracted from peer-reviewed scientific papers and
non-scientific text to assign a score that indicates the scientificness of a
sentence. We investigate the effect of equations and citations on this score to
test the model for potential biases. Secondly, we create a mapping of section
titles to a standard paper layout in AI and machine learning to classify a
sentence to its most likely section. We study the impact of context, \ie
surrounding sentences, on the section classification performance. Finally, we
propose a paraphraser, which suggests an alternative for a given sentence that
includes word substitutions, additions to the sentence, and structural changes
to improve the writing style. We train various large language models on
sentences extracted from arXiv papers that were peer reviewed and published at
A*, A, B, and C ranked conferences. On the scientificness task, all models
achieve an MSE smaller than $2\%$. For the section classification, BERT
outperforms WideMLP and Sci\-BERT in most cases. We demonstrate that using
context enhances the classification of a sentence, achieving up to a $90\%$
F1-score. Although the paraphrasing models make comparatively few alterations,
they produce output sentences close to the gold standard. Large fine-tuned
models such as T5 Large perform best in experiments considering various
measures of difference between input sentence and gold standard. Code is
provided: https://github.com/JustinMuecke/SciSen.

本文提出了一种基于回归模型的科技句子识别方法，研究了科技公式和引文对于科技性评分的影响，构建了一种 AI 和机器学习标准文章版面的映射，提出了一种改进写作风格的软件，并训练了各种大型语言模型。

科技写作支持的语言模型微调

Fine-Tuning Language Models for Scientific Writing Support

Lexical substitution (LS) aims at finding appropriate substitutes for a
target word in a sentence. Recently, LS methods based on pretrained language
models have made remarkable progress, generating potential substitutes for a
target word through analysis of its contextual surroundings. However, these
methods tend to overlook the preservation of the sentence's meaning when
generating the substitutes. This study explores how to generate the substitute
candidates from a paraphraser, as the generated paraphrases from a paraphraser
contain variations in word choice and preserve the sentence's meaning. Since we
cannot directly generate the substitutes via commonly used decoding strategies,
we propose two simple decoding strategies that focus on the variations of the
target word during decoding. Experimental results show that our methods
outperform state-of-the-art LS methods based on pre-trained language models on
three benchmarks.

本研究探究了如何利用重述生成器从众多的替换候选词中生成一个最优的替换候选词的问题， 提出了两种解码策略， 实验结果表明，这些策略能够在所作的基准测试中胜过基于预训练语言模型的现有的词汇替换方法。