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, i.e., 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 SciBERT 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 under https://github.com/JustinMuecke/SciSen.

翻译：我们为科学写作者提供支持，帮助他们判断一个句子是否具有科学性、属于论文的哪个章节，并提出改进句子的释义建议。首先，我们提出一个回归模型，该模型在从同行评审科学论文中提取的科学句子与非科学文本构成的语料库上进行训练，以分配一个表示句子科学性的分数。我们研究了方程和引用对该分数的影响，以测试模型潜在的偏差。其次，我们创建了章节标题到人工智能与机器学习领域标准论文布局的映射，用于将句子分类到其最可能的章节。我们研究了上下文（即周围句子）对章节分类性能的影响。最后，我们提出一个释义器，该释义器为给定句子提供替换建议，包括词语替换、句子补充和结构修改，以改进写作风格。我们对从arXiv论文（经过同行评审并发表在A*、A、B、C级会议上）中提取的句子，训练了多种大型语言模型。在科学性任务上，所有模型的均方误差均小于2%。对于章节分类，BERT在大多数情况下优于WideMLP和SciBERT。我们证明，使用上下文可以增强句子的分类性能，F1分数最高达到90%。尽管释义模型所做的修改相对较少，但其输出句子接近金标准。在考虑输入句子与金标准之间多种差异度量的实验中，T5 Large等大型微调模型表现最佳。代码可在https://github.com/JustinMuecke/SciSen 获取。