WriteWise: software that guides scientific writing
Eduardo N. Fuentes1,3*, Hector Allende-Cid2, Sebastián Rodríguez1, Rene Venegas2, Juan Pavez1,
Wenceslao Palma2, Ismael Figueroa, Sofia Zamora2, Brayn Diaz1, Ashley VanCott3,
1WriteWise, WriteWise Research Group, Artificial Intelligence Unit, Santiago, Chile
2Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
3BioPub, Scientific Writing Unit, Santiago, Chile
*Corresponding author: Dr. Eduardo N. Fuentes; email: email@example.com
Writing scientific articles is traditionally learnt through an inefficient, slow, and arduous process of trial and error.
This despite the fact that writing a scientific article is not trivial and in general there is a lack of formal training for
learning this task. Scientific articles are inherently complex to write, mainly due to the fact that they are highly
technical and present a linguistic/stylistic format specific to the academic genre.
Currently, a scarce number of technological approaches have tried to solve this problem. Most
recognizable softwares used by scientists to write papers are Word, GoogleDocs, Grammarly, Authorea, Overleaf,
among others. However, none of these solutions satisfactorily provide specific feedback on academic genre,
teaches about it, and aids in writing high linguistic levels (academic discourse).
To address this fundamental issue in the academic world we have developed a new software based on
artificial intelligence that tackles all the aforementioned problematics; therefore, helping and teaching researchers
how to write high-quality English manuscripts.
Several corpora were used, but the most relevant for the development of the software were: 1) An unlabeled
“Gold Standard” which consists of 2116 papers; 2) A labeled “Gold Standard” which consists of approximately
13,000 sentences. The “Gold Standard” consists of papers in life-sciences that contain top scientometrics, have
in-housed copy editors with rigorous editorial processes, and that present the discursive structure prototypical of
2.2. System for tagging texts at the discursive level
A linguistic model for writing scientific texts in the life-sciences was generated. In parallel a computational system
was used to subsequently tag sentences at rhetorical-discursive, structural, lexical-grammatical, stylistic, and
functional elements prototypical to this genre.
2.3. Machine learning models
Several shallow and deep learning models were used for text representation (e.g. word and sentence embeddings),
visualization (graphs), and extraction of the most relevant information needed for discourse segmentation, and to
assess text coherence and cohesion.
2.4. Back and Front end development
Write Wise is presented as a modern web application implemented in proven technologies: Vue.js and Django for
frontend and backend development, respectively. The system has a scalable architecture where the machine
learning models are consumed as microservices inside the web application.
The Write Wise platform can be subdivided into three modules that tackle the main problems while scientist write
papers: 1) sentence length; 2) text order and organization; 3) discourse.
The first module (sentence length) provide information on the microstructure of a text, identifying the
number of word per each sentence. The second module (text order and organization) automatically detects the
similarity between consecutive sentences in the text. We identified an optimal N° words/sentences and semantic
similarity threshold based on analysis of the unlabeled Gold Standard. With these modules the user can compare
each sentence with the unlabeled Gold Standard. If the user exceeds the threshold, is notified; thus, this person
can make changes in the text. Thanks to these two modules the system helps the user to transmit one idea or
message per sentence using clear, precise, and concise sentences, which are consecutively connected. The third
and most sophisticated module (discourse) provides the user with examples of different “discursive steps” (i.e.
functional linguistic unit that fulfills a communicative purpose in a sentence). Thanks to this the user can write a
logical and well-structured text at a high linguistic level.
WriteWise represents the first commercially available advanced platform that provides user´s help and feedback to
improve scientific papers writing. This is thanks to the development of and advance textual data representation at
different linguistic levels (e.g. words, sentences) through using cutting-edge machine-learning models and applied