Automated Evaluation of Scientific Writing (AESW) Shared Task 2016
An Automated Evaluation of Scientific Writing (AESW) Shared Task 2016 is an NLP shared task that ...
References
2016
- http://textmining.lt/aesw/
- QUOTE: The vast number of scientific papers being authored by non-native English speakers creates a large demand for effective computer-based writing tools to help writers compose scientific articles. Several shared tasks have been organized (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013; Ng et al., 2014), constituting a major step toward evaluating the feasibility of building novel grammar error correction technologies. English language learner (ELL) corpora were made available for research purposes (Dahlmeier et al., 2013; Yannakoudakis et al., 2011). An extensive overview of the feasibility of automated grammatical error detection for language learners was conducted by Leacock et al. (2010). While these achievements are critical for language learners, we also need to develop tools that support genre-specific writing features. The shared task focuses on the genre of scientific writing.
The goal of the Automated Evaluation of Scientific Writing (AESW) Shared Task is to analyze the linguistic characteristics of scientific writing to promote the development of automated writing evaluation tools that can assist authors in writing scientific papers. The task is to predict whether a given sentence requires editing to ensure its 'fit' within the scientific writing genre.
A few words should be said about the evaluation of scientific writing. Some proportion of ‘corrections’ in the shared task data are real error corrections (such as wrong pronoun, as well as various other grammatical and stylistic errors), but some almost certainly represent style issues and similar ‘matters of opinion’. And it seems unfair to expect someone to spot these. This is because of different language editing traditions, experience, and ... the absence of uniform agreement of what ‘good’ language should look like. Nevertheless, your participation in the shared task will certainly help spotting the characteristics of ‘good’ scientific language, and help create a consensus of which language improvements are acceptable.
- QUOTE: The vast number of scientific papers being authored by non-native English speakers creates a large demand for effective computer-based writing tools to help writers compose scientific articles. Several shared tasks have been organized (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013; Ng et al., 2014), constituting a major step toward evaluating the feasibility of building novel grammar error correction technologies. English language learner (ELL) corpora were made available for research purposes (Dahlmeier et al., 2013; Yannakoudakis et al., 2011). An extensive overview of the feasibility of automated grammatical error detection for language learners was conducted by Leacock et al. (2010). While these achievements are critical for language learners, we also need to develop tools that support genre-specific writing features. The shared task focuses on the genre of scientific writing.
2016b
- (Schmaltz et al., 2016) ⇒ Allen Schmaltz, Yoon Kim, Alexander M Rush, and Stuart Shieber. (2016). “Sentence-Level Grammatical Error Identification As Sequence-to-Sequence Correction.” In: Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications.
- QUOTE: We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. …