Neural approaches for writing assistant tasks


  • O. H. Skurzhanskyi Taras Shevchenko National University of Kyiv
  • A. A. Marchenko Taras Shevchenko National University of Kyiv



natural language processing, neural networks, machine learning, writing assistant, paraphrasing, grammar correction, text simplification


The article is devoted to the analysis of tasks for building a writing assistant, one of the most prominent fields of natural language processing and artificial intelligence in general. Specifically, we explore monolingual local sequence transduction tasks: grammatical and spelling errors correction, text simplification, paraphrase generation. To give a better understanding of the considered tasks, we show examples of expected rewrites. Then we take a deep look at such key aspects as existing publicly available datasets and their training splits, quality metrics for high quality evaluation, and modern solutions based primarily on neural networks. For each task, we analyze its main peculiarities and how they influence the state-of-the-art models. Eventually, we investigate the most eloquent shared features for the whole group of tasks in general and for approaches that provide solutions to them.

Pages of the article in the issue: 232 - 238

Language of the article: Ukrainian


OUYANG L. et al. (2022) Training language models to follow instructions with human feedback In NeurIPS 2022.

BRYANT, C. et al. (2019) The BEA-2019 Shared Task on Grammatical Error Correction. In ACL 2019.

DAHLMEIER, D. and NH, T. H. (2012) Better Evaluation for Grammatical Error Correction. In NAACL 2012.

TAJIRI, T. et al. (2012) Tense and Aspect Error Correction for ESL Learners Using Global Context. In ACL 2012.

YANNAKOUDAKIS, H. et al. (2011) A New Dataset and Method for Automatically Grading ESOL Texts. In ACL 2011.

DAHLMEIER, D. and NH, T. H. (2012) Better Evaluation for Grammatical Error Correction. In NAACL 2012.

BRYANT, C. et al. (2017) Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction. In ACL 2017.

ZHAO, W. et al. (2019) Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data. In NAACL 2019.

STAHLBERG F. and KUMAR S. (2021) Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models. In BEA 2021.

XU, W. et al. (2015) Problems in Current Text Simplification Research: New Data Can Help. In TACL 2015.

ZHANG, X. and LAPATA, M. (2017) Sentence Simplification with Deep Reinforcement Learning. In EMNLP 2017.

XU, W. et al. (2016) Optimizing Statistical Machine Translation for Text Simplification. In TACL 2016.

KINCAID, J. P. et al. (1975) Derivation Of New Readability Formulas. Institute for Simulation and Training, 56.

ZHAO, S. et al. (2018) Integrating Transformer and Paraphrase Rules for Sentence Simplification. In EMNLP 2018.

PAVLICK, E. and CALLISON-BURCH, C. (2016) Simple PPDB: A Paraphrase Database for Simplification. In ACL 2016.

OMELIANCHUK, K. et al. (2021) Text Simplification by Tagging. In BEA 2021.

OMELIANCHUK, K. et al. (2019) GECToR – Grammatical Error Correction: Tag, Not Rewrite. In BEA 2019.

YANG, Z. et al. (2019) XLNet: Generalized Autoregressive Pretraining for Language Understanding. In NeurIPS 2019.

WIETING, J. and GIMPEL, K. (2017) PARANMT-50M: Pushing the Limits of Paraphrastic Embeddings with Millions of Machine Translations. In ACL 2017.

KAGGLE. (2017) Quora Duplicate Questions [Online] – Available from: [Accessed: 19th June 2012].

LIN, T. et al. (2014) Microsoft COCO: Common Objects in Context. In ECCV 2014.

PAPINENI, K. et al. (2002) Bleu: a Method for Automatic Evaluation of Machine Translation. In ACL 2002.

SATANJEEV B., ALON L. (2005) METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. In ACL 2005.

GUO, Y. et al. (2019) Paraphrase Generation with Multilingual Language Models. In ACL 2019.

PARTO, B. N. et al. (2018) Learning Semantic Sentence Embeddings using Pair-wise Discriminator Models. In COLING 2019.




How to Cite

Skurzhanskyi, O. H., & Marchenko, A. A. (2023). Neural approaches for writing assistant tasks. Bulletin of Taras Shevchenko National University of Kyiv. Physical and Mathematical Sciences, (2), 232–238.



Computer Science and Informatics