# Towards Lithuanian grammatical error correction

arXiv: https://arxiv.org/abs/2203.09963 · HTML: https://mlsanity.com/paper/2203.09963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study](https://mlsanity.com/paper/1807.01270/) (2018)
- [Neural Language Correction with Character-Based Attention](https://mlsanity.com/paper/1603.09727/) (2016)
- [Czech Grammar Error Correction with a Large and Diverse Corpus](https://mlsanity.com/paper/2201.05590/) (2022)
- [Phrase-based Machine Translation is State-of-the-Art for Automatic Grammatical Error Correction](https://mlsanity.com/paper/1605.06353/) (2016)
- [The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction](https://mlsanity.com/paper/1906.01733/) (2019)
- [Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation](https://mlsanity.com/paper/1804.05945/) (2018)
- [Correcting diacritics and typos with a ByT5 transformer model](https://mlsanity.com/paper/2201.13242/) (2022)
- [Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation](https://mlsanity.com/paper/2005.01158/) (2020)
- [Generating abstractive summaries of Lithuanian news articles using a transformer model](https://mlsanity.com/paper/2105.03279/) (2021)

## Built on by (most cited first, 2 of 2)

- [Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora](https://mlsanity.com/paper/2305.17906/) (2023)
- [Sentiment Analysis of Lithuanian Online Reviews Using Large Language Models](https://mlsanity.com/paper/2407.19914/) (2024)

## Abstract (arXiv)

Everyone wants to write beautiful and correct text, yet the lack of language skills, experience, or hasty typing can result in errors. By employing the recent advances in transformer architectures, we construct a grammatical error correction model for Lithuanian, the language rich in archaic features. We compare subword and byte-level approaches and share our best trained model, achieving F0.5=0.92, and accompanying code, in an online open-source repository.