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Analysis and news


Language as a barrier to publishing? Hilde van Zeeland and Juan Castro describe how natural language processing is changing the game


Many researchers, especially those who speak English as a second language, struggle with writing papers. The area of natural language processing (NLP), a strand of artificial intelligence (AI), can help to tackle this issue. It offers a range of tech initiatives that facilitate the understanding, writing, and proofreading of scientific texts. In this piece, we look at how NLP is on its way to transform scientific writing and publishing. NLP is incredibly broad as a term, and


its use cases are infinite. In scientific publishing it is typically used to derive information or patterns from manuscripts, and to classify, summarise, translate or proofread them. NLP tech can support authors with any research task that involves language, from digesting relevant papers to writing their own.


Finding and evaluating literature There are plenty of NLP-driven tools that help researchers process the literature. Examples are Paper Digest, which auto-summarises articles, sci.AI, which lets researchers find relations between objects across papers, and scite, which shows how often an article has been cited with supporting or contrasting evidence. Initiatives such as these help researchers to find, digest, relate and position articles, and have the potential not only to alleviate researchers’ workload but to contribute to science overall. With more data becoming available and NLP making progress at incredible pace, the semantic analysis of articles may lead to even more impactful applications for literature evaluation. Josh Nicholson, CEO and founder of


scite, says: ‘I think articles are going to be increasingly used in new ways that make them more discoverable and easier to understand. Just like with cell phones as new capabilities became available, we started to use our phones for calling less and less. I see this happening with articles now, where new capabilities are being introduced that allow researchers to go beyond simply printing them. For example, we already have AI tools that can help


24 Research Information October/November 2021


researchers pull out key facts from papers and distill the findings into short synopses, and that can show you how papers have been discussed in literature or social media.’


Another NLP-driven area that can


help researchers with reviewing the literature is text simplification. While publicly accessible text simplification tools are typically limited to word-level changes (replacing technical terms with higher-frequency ones, and so on), state-of-the-art approaches provide full sentence rewrites. Services built on these approaches may be especially relevant to junior researchers or non-native English speakers who struggle to understand scientific papers. As many of such approaches are open source, they might become more visible within the research ecosystem over time, as developers build user interfaces to query them.


NLP for automated proofreading NLP can also be a powerful asset to facilitate the writing process. Researchers already have several language apps at


“Semantic analysis of articles may lead to even more impactful applications for literature evaluation”


their disposal, such as the grammar and spell check within Google Docs and Microsoft Word, and stand-alone services such as Grammarly and Writefull. Writefull, which offers automated proofreading solutions to researchers and publishers, has quickly gained popularity over the last few years thanks to NLP tech that is tailored to scientific writing. The company recently reported that its language models have achieved 88 per cent of the average human proofreader’s performance, making affordable and high-quality proofreading widely available to researchers. AI-driven proofreading solutions are


increasingly adopted by publishers, too. They use them to triage manuscripts


based on language quality, to speed up the peer-reviewing process, and to alleviate the work of their copy editors. Juan Castro, CEO and co-founder of Writefull, witnesses a changing attitude towards AI-driven proofreading in the publishing industry: ‘Publishers and scientific copy- editing companies often assess our tech before they integrate it into their systems. Several customers have told us that the language edits given by our models are more consistent and accurate than those of their human copy editors. While publishers were sceptical of our tech a few years ago, we see a rapid shift in this, as its capabilities become evident.’ Further uptake of AI-driven proofreading


tools can help to reduce the linguistic bias in scientific publishing, where submissions with ‘non-native-like’ language are perceived as having lower scientific quality[1]


. Hindawi, the first publisher to


integrate Writefull, writes on its website that it offers the service to its authors ‘to help ensure no one is unfairly held back from publishing due to English not being a first language’. As the adoption of NLP- based proofreading progresses, language should become less of a barrier to publishing, diminishing the divide between more and less language-proficient researchers.


Auto-generated manuscripts Automated proofreading services can also be used to complement other NLP-driven applications, such as those that automatically generate language or translate texts. There have been a number of such initiatives within scientific writing, including an automatically generated book and tools to auto-generate (parts of) manuscripts, like SciGen and SciNote’s Manuscript Writer. Beyond academia, GPT-3, DeepL and Google Translate are powerful tools to auto-generate or auto- translate language. Big players such as Google, Microsoft and Amazon regularly publish NLP-based resources that can make an impact on scientific writing. For example, Google Research recently launched a service called Tapas, where users can ask a question about a table and get an auto-generated answer back


@researchinfo | www.researchinformation.info


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