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This page shows all publications in the ACL anthology investigating spell checkers or other proofing tools (articles referring to spell or proof in the abstract, where the spell checker or proofing tool is the topic of the article, rather than e.g. a tool uset for evaluation purposes).
ring which is a dialect of the North Frisian language spoken on the island of Sylt in the North Frisia region of Germany. S{\"o}lring is estimated to have only hundreds of native speakers and very limited online language resources making it a prime candidate for language preservation initiatives. To help preserve S{"o}lring and provide resources for S{\"o}lring speakers and learners, we built an online dictionary. Our dictionary, called friisk.org, provides translations for over 28,000 common German words to S{"o}lring. In addition, our dictionary supports translations for S{\"o}lring to German, spell checking for S{"o}lring, conjugations for common S{\"o}lring verbs, and an experimental transcriber from S{"o}l`ring to IPA for pronunciations. Following the release of our online dictionary, we collaborated with neighboring communities to add limited support for additional North Frisian dialects including Fering, Halligen Frisian, Karrharder, Nordergoesharder, {"O}{"o}mrang, and Wiedingharder.}kommentar = “stavekontrollen her er ein online-stavekontroll (mat inn eitt og eitt ord) som tar ei fullformordliste som input og foreslår ord frå lista til feilskrivne ord”
s language. For applications like automatic spell checker selection, language identification must use very short strings such as text message fragments. In this work, we reproduce a language identification architecture that Apple briefly sketched in a blog post. We confirm the bi-LSTM models performance and find that it outperforms current open-source language identifiers. We further find that its language identification mistakes are due to confusion between related languages.”s evaluation process. The corpus was then automatically updated to synthetically include realistic errors to produce a training and evaluation ground truth comparison. We designed and evaluated two supervised baseline WikiFixer error correction methods: (1) a naive approach based on a maximum likelihood character-level language model; (2) and an advanced model based on a sequence-to-sequence (seq2seq) neural network architecture. Both error correction models operated at a character level. When compared against an off-the-shelf word-level spell checker these methods showed a significant improvement in the tasks performance {–} with the seq2seq-based model correcting a higher number of errors than it introduced. Finally, we published our data and code.”abstract = “For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model`s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.”
“Heterogeneous Recycle Generation for Chinese Grammatical Error Correction”, - abstract = “Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to properly train. Furthermore, NMT-based systems treat GEC purely as a translation task and overlook the editing aspect of it. In this work we propose a heterogeneous approach to Chinese GEC, composed of a NMT-based model, a sequence editing model, and a spell checker. Our methodology not only achieves a new state-of-the-art performance for Chinese GEC, but also does so without relying on data augmentation or GEC-specific architecture changes. We further experiment with all possible configurations of our system with respect to model composition order and number of rounds of correction. A detailed analysis of each model and their contributions to the correction process is performed by adapting the ERRANT scorer to be able to score Chinese sentences.”
abstract = “We present an algorithm for automatic correction of spelling errors on the sentence level, which uses noisy channel model and feature-based reranking of hypotheses. Our system is designed for Russian and clearly outperforms the winner of SpellRuEval-2016 competition. We show that language model size has the greatest influence on spelling correction quality. We also experiment with different types of features and show that morphological and semantic information also improves the accuracy of spellchecking.”
abstract = “Part-of-speech (POS) tagging and chunking have been used in tasks targeting learner English; however, to the best our knowledge, few studies have evaluated their performance and no studies have revealed the causes of POS-tagging/chunking errors in detail. Therefore, we investigate performance and analyze the causes of failure. We focus on spelling errors that occur frequently in learner English. We demonstrate that spelling errors reduced POS-tagging performance by 0.23{\%} owing to spelling errors, and that a spell checker is not necessary for POS-tagging/chunking of learner English.”