Papers with language learners
Learning to Understand Child-directed and Adult-directed Speech (2020.acl-main)
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| Challenge: | linguistic properties of child-directed speech differ from adult-directed in many ways . linguistic differences between CDS and ADS are retained, but the acoustic properties are similar. |
| Approach: | They compare the task performance of models trained on adult-directed speech and child-directed language . they propose that CDS is optimized for learnability, but not for comprehension . |
| Outcome: | The proposed model trains on adult-directed speech and child-directed language . the model generalizes better on the training register and on synthesized speech . |
Investigating Critical Period Effects in Language Acquisition through Neural Language Models (2025.tacl-1)
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| Challenge: | Scholars of human development have long debated whether these phenomena are predetermined by innately encoded developmental changes in the maturing brain or natural consequences of increased experience. |
| Approach: | They use language models to test whether CP effects are peculiar to humans . they find that LMs do not show CP when L2 exposure is delayed . scholars have long debated whether innate maturation changes predetermine CP . |
| Outcome: | The proposed model does not show CP effects when the age of exposure of L2 is delayed. |
ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction (2022.naacl-main)
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| Challenge: | Currently available grammatical error correction datasets focus on written essays . a novel dataset is presented to improve the accuracy of existing educational chatbots . |
| Approach: | They propose a novel grammatical error correction dataset using essays and other long-form text written by language learners. |
| Outcome: | The proposed dataset improves the performance of a conversational chatbot in a human-machine conversational setting. |
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency (2020.acl-srw)
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| Challenge: | Recent studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. |
| Approach: | They propose to extract sentences similar to those written by language learners and generate pseudo errors by considering error types that learners often make. |
| Outcome: | The proposed model significantly improves the performance of the Russian GEC task compared with other models using pseudo data. |
MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)
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| Challenge: | Metaphors do not take literal meanings in contexts, which may cause difficulties for language learners and machines to understand them. |
| Approach: | They propose a computational metaphor processing online system that queries metaphoricity labels, paraphrases and concept mappings for non-domain-specific text. |
| Outcome: | The proposed system can query metaphoricity labels, paraphrases, and concept mappings for non-domain-specific text without coding background. |
LanguageNet: Learning to Find Sense Relevant Example Sentences (C18-2)
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| Challenge: | LanguageNet is a system that can help second language learners to search for different meanings and usages of a word . the polysemy of words, namely words with more than one sense, is one of the major challenges for ESOL learners . |
| Approach: | They propose a system which can help second language learners to search for different meanings of a word. |
| Outcome: | The proposed system can help second language learners to search for different meanings and usages of a word. |
WantWords: An Open-source Online Reverse Dictionary System (2020.emnlp-demos)
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| Challenge: | Existing reverse dictionary systems only support English reverse dictionary queries . a reverse dictionary can help people who can't remember a word from memory . |
| Approach: | They propose an online reverse dictionary system that outperforms other reverse dictionary systems . it supports Chinese and English-Chinese as well as Chinese-English cross-lingual reverse dictionary queries . |
| Outcome: | The proposed reverse dictionary outperforms other reverse dictionary systems on performance . it supports Chinese and English-Chinese as well as Chinese-English queries . |
Camelira: An Arabic Multi-Dialect Morphological Disambiguator (2022.emnlp-demos)
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| Challenge: | Camelira is a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Approach: | They propose a web-based Arabic multi-dialect morphological disambiguation tool that covers modern standard Arabic, Egyptian, Gulf, and Levantine. |
| Outcome: | The proposed tool covers modern standard Arabic, Egyptian, Gulf, and Levantine . it also provides an option to automatically choose an appropriate disambiguator based on the prediction of a dialect identification component. |
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)
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Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Karën Fort, Katerina Zdravkova, Iztok Kosem, Jaka Čibej, Špela Arhar Holdt, Alice Millour, Alexander König, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
| Challenge: | Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones. |
| Approach: | They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises . |
| Outcome: | The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs. |
Cloze Quality Estimation for Language Assessment (2023.findings-eacl)
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| Challenge: | Cloze tests are widely used in language proficiency tests, but they suffer from low quality and low reliability. |
| Approach: | They propose a task to evaluate whether a cloze test is of sufficient "high-quality" they use a dataset that includes English clozing tests and corresponding evaluations by native English speakers. |
| Outcome: | The proposed method could contribute to the CQE task, but the task is still challenging. |
Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)
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Elham Akhlaghi, Branislav Bédi, Fatih Bektaş, Harald Berthelsen, Matthias Butterweck, Cathy Chua, Catia Cucchiarin, Gülşen Eryiğit, Johanna Gerlach, Hanieh Habibi, Neasa Ní Chiaráin, Manny Rayner, Steinþór Steingrímsson, Helmer Strik
| Challenge: | LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Approach: | They propose to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Outcome: | The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening. |
SRS-Stories: Vocabulary-constrained multilingual story generation for language learning (2025.emnlp-industry)
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| Challenge: | Existing methods for learning foreign languages are to use a spaced repetition system to learn new vocabulary. |
| Approach: | They use large language models to generate personalized stories using only the vocabulary they know. |
| Outcome: | The generated stories are more grammatical, coherent, and provide better examples of word usage than the standard beam search approach. |
Babysit A Language Model From Scratch: Interactive Language Learning by Trials and Demonstrations (2025.naacl-long)
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| Challenge: | Recent advances in large language models have adopted a non-interactive training paradigm, and refined pre-trained models through feedback afterward. |
| Approach: | They propose a trial-and-demonstration learning framework that incorporates student trials, teacher demonstrations, and a reward conditioned on language competence at various developmental stages. |
| Outcome: | The proposed framework accelerates word acquisition for student models of equal and smaller numbers of parameters and a strong correlation between the frequency of words in trials and learning curves. |
SW4ALL: a CEFR Classified and Aligned Corpus for Language Learning (L18-1)
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| Challenge: | Learning a second language requires exposition to texts, especially for the acquisition of vocabulary. |
| Approach: | They propose a corpus of documents classified by language proficiency level . they use alignments between the English Wikipedia and the Simple English Wikipedia . |
| Outcome: | The SW4ALL corpus contains 8,669 pairs of documents that present different levels of proficiency. |
LeSpell - A Multi-Lingual Benchmark Corpus of Spelling Errors to Develop Spellchecking Methods for Learner Language (2022.lrec-1)
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| Challenge: | Existing spellcheckers do not work well with learner data. |
| Approach: | They propose a multi-lingual evaluation data set of spelling mistakes in context that is highly customizable for the DKPro architecture. |
| Outcome: | The proposed spellchecker improves performance in many settings and can be customized to meet learners' needs. |
BAREC Demo: Resources and Tools for Sentence-level Arabic Readability Assessment (2025.emnlp-demos)
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| Challenge: | Existing efforts to assess the readability of Arabic text are limited due to its rich morphology, complex syntax, and ambiguous orthography. |
| Approach: | They propose a web-based system for fine-grained, sentence-level Arabic readability assessment. |
| Outcome: | The demo provides two main functionalities for educators, content creators, language learners, and researchers: (1) a Search interface to explore the annotated dataset for text selection and resource development; (2) an Analyze interface to assign detailed readability labels to Arabic texts at the sentence level. |
Building an English Vocabulary Knowledge Dataset of Japanese English-as-a-Second-Language Learners Using Crowdsourcing (L18-1)
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| Challenge: | a dataset for analyzing the English vocabulary of English-as-a-second language learners is available . a vocabulary size test was performed by 100 test takers hired via crowdsourcing . |
| Approach: | They propose a dataset for analyzing the English vocabulary of English-as-a-second language learners. |
| Outcome: | a dataset for analyzing the English vocabulary of English-as-a-second language learners is available online . the results show that the test is reliable and can be predicted with high accuracy . |
Semi-automatically Annotated Learner Corpus for Russian (2022.lrec-1)
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| Challenge: | Revita Learner Corpus is a semi-automatically annotated learner corpus for Russian . it is used for research in second language acquisition and foreign language teaching . |
| Approach: | They propose a semi-automatically annotated learner corpus for Russian that detects errors automatically and annotates errors by type. |
| Outcome: | The proposed corpus detects errors automatically and is annotated by type . the data is made public and the process is much cheaper and faster . |
BabyLM’s First Constructions: Causal interventions provide a signal of learning (2025.emnlp-main)
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| Challenge: | Recent work shows sensitivity to constructions in pretrained language models, but their relevance to human language learning is doubted. |
| Approach: | They use construction grammars to demonstrate sensitivity to constructions in pretrained language models. |
| Outcome: | The proposed models learn diverse constructions even hard cases that are superficially indistinguishable. |
Evaluating language models for the retrieval and categorization of lexical collocations (2021.eacl-main)
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| Challenge: | Lexical collocations are idiosyncratic combinations of two syntactically bound lexical items. |
| Approach: | They perform an exhaustive analysis of current language models for collocation understanding . they first construct a dataset of apparitions of lexical collocations in context . |
| Outcome: | The proposed models perform well in distinguishing light verb constructions, especially if the collocation’s first argument acts as subject, but often fail to distinguish, first, different syntactic structures within the same semantic category, and second, fine-grained semantic categories which restrict the use of small sets of valid collocates for a given base. |
Interactive Word Completion for Plains Cree (2022.acl-long)
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| Challenge: | a tool that helps users incrementally build complex words is being developed in morphologically complex languages. |
| Approach: | They propose a finite state approach which maps prefixes in a language to completions up to the next morpheme boundary for incremental building of complex words. |
| Outcome: | The proposed approach shows portability to a larger, more complete morphological transducer. |
CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)
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| Challenge: | Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar. |
| Approach: | They propose to use cross-lingual explanations of idiomatic expressions to support vocabulary expansion for language learners. |
| Outcome: | The proposed system is able to explain idiomatic expressions in non-standard language. |
Negative language transfer in learner English: A new dataset (2021.naacl-main)
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| Challenge: | This dataset contains annotated error causes for learner writing errors that tie learner mistakes to structures from their first language. |
| Approach: | They propose a learner English dataset enhanced with annotated error causes and concrete examples of learner errors that relate to their first languages. |
| Outcome: | The proposed dataset will be used to analyze learner errors related to language transfer from the learners’ first language. |
Co-evolution of language and agents in referential games (2021.eacl-main)
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| Challenge: | Referential games allow neural agents to learn language, but they do not take into account the learning biases of the learners. |
| Approach: | They propose to model cultural and architectural evolution in a population of agents to take into account learning biases of the language learners and let them co-evolve. |
| Outcome: | The proposed model outperforms cultural transmission in a population of agents and takes into account learning biases of the learners. |
Self-Supervised Curriculum Learning for Spelling Error Correction (2021.emnlp-main)
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| Challenge: | Current approaches to SEC typically leverage a pre-training then fine-tuning procedure that treats data equally. |
| Approach: | They propose a self-supervised curriculum learning approach to improve model performance and model learning. |
| Outcome: | The proposed approach improves the model training and improves CL measurement. |
Personalized Text Retrieval for Learners of Chinese as a Foreign Language (C18-1)
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| Challenge: | a personalized text retrieval algorithm helps language learners select the most suitable reading material in terms of vocabulary complexity. |
| Approach: | They propose a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. |
| Outcome: | The proposed algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficient learners. |
Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning (C18-1)
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| Challenge: | Literature on receptive and productive vocabulary often ignores grammar in second language acquisition studies. |
| Approach: | They use two corpora to investigate divergences in grammatical structures in texts . they set a polarity to the divergence scores to indicate whether there is overuse or underuse . |
| Outcome: | The proposed system will help language learners to activate more of their passive knowledge in writing texts. |
Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task (2024.lrec-main)
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| Challenge: | Existing methods to assess lexical complexity are used to evaluate the difficulty of vocabulary for language learners. |
| Approach: | They propose to use pre-trained language models to assess the complexity of a word based on its context. |
| Outcome: | The proposed method outperforms the best systems in SemEval-2021. |
Using Neural Machine Translation for Generating Diverse Challenging Exercises for Language Learner (2023.acl-long)
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| Challenge: | a common challenge for language learners is understanding how to appropriately use words that may have similar meanings but are used in different contexts. |
| Approach: | They propose a method to automatically generate distractors for cloze exercises for English language learners using round-trip neural machine translation. |
| Outcome: | The proposed method generates distractors for cloze exercises for English learners . it shows that the generated distractors are of the same difficulty as human distractors . |
Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
| Approach: | They propose to use an automated speech recognition module to train language learners' speaking skills on spontaneous speech by young language learners. |
| Outcome: | The proposed model improves on 85 hours of English audio spoken by Swiss learners and preserves their mistakes. |
MIAPARLE: Online training for the discrimination of stress contrasts (L18-1)
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| Challenge: | Second language learners tend to imprint the prosody of their mother language onto the second language (L2) . this can hamper communication between learners and natives, and can also affect the credibility of learners and how they are evaluated by others. |
| Approach: | They propose a tool that focuses on stress perception for speakers whose L1 is a fixed-stress language, such as French. |
| Outcome: | The tool is particularly useful for speakers whose L1 is a fixed-stress language, such as French. |
Automatically Building a Multilingual Lexicon of False Friends With No Supervision (2020.lrec-1)
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| Challenge: | a method to detect false friends from cognates is developed . cognates are words in genetically related languages with a common proto-word . in some cases, cognates have diverged from the common etymon and their meanings became different from each other. |
| Approach: | They propose an automatic method to detect false friends from a set of cognates . cognates are words in genetically related languages which derive from etymons . authors propose a measure of "falseness" of a false friends pair based on cross-lingual word embeddings based in the system . |
| Outcome: | The proposed method can be extended to any language pair, with monolingual corpora and a bilingual dictionary. |
Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus (2020.lrec-1)
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| Challenge: | Adding semantic role labels to the NPCMJ will help language learners and linguists search for syntactic and semantic features. |
| Approach: | They propose to add frame information for predicates and two types of semantic role labels that mark contributions of arguments to the NINJAL Parsed Corpus of Modern Japanese (NPCMJ) this will provide a web-accessible language resource for linguists and language learners searching for syntactic and semantic features. |
| Outcome: | The proposed framework will be able to search examples of Japanese for syntactic and semantic features. |
Collocations in Russian Lexicography and Russian Collocations Database (2020.lrec-1)
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| Challenge: | Existing methods for collocation extraction cannot be considered perfect, argues a new study. |
| Approach: | They propose to build a database that will include dictionary and statistical collocations in Russian . the database will be based on dictionaries and online systems that describe collocation . |
| Outcome: | The proposed database will include dictionary and statistical collocations in Russian . the results can be useful for machine learning and for other NLP tasks . |
Generation of a Spanish Artificial Collocation Error Corpus (L18-1)
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| Challenge: | collocations are combinations of two elements where one (the base) is freely chosen, despite the limitations of the other (collocate) current tools for collocation error detection and correction focus on collocation validation and identification of miscollocations . |
| Approach: | They propose an algorithm for automatic generation of an artificial collocation error corpus of american English learners of Spanish that includes 17 different types of collocation errors. |
| Outcome: | The proposed algorithm can detect and classify collocation errors in learners' writings . collocation error detection and correction has not received the attention it deserves . |
The CLARIN Knowledge Centre for Atypical Communication Expertise (2020.lrec-1)
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| Challenge: | ACE is a new knowledge center for Atypical communication experts . it is located at the Centre for Language and Speech Technology (CLST) at Radboud University . |
| Approach: | They introduce a new CLARIN Knowledge Center called the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive at the Max Planck Institute for Psycholinguistics to safeguard GDPR-compliant data storage and access. |
| Outcome: | The new CLARIN Knowledge Center is the K-Centre for Atypical Communication Expertise (ACE) ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. |
Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)
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| Challenge: | Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them. |
| Approach: | They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts. |
| Outcome: | The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%. |
Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)
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| Challenge: | To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors. |
| Approach: | They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections. |
| Outcome: | The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule. |
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)
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| Challenge: | Existing GEC models produce spurious corrections or fail to detect lots of errors. |
| Approach: | They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph . |
| Outcome: | The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets. |
CEPOC: The Cambridge Exams Publishing Open Cloze dataset (2022.lrec-1)
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| Challenge: | This paper presents the first dataset of open cloze tests for language learners at different proficiency levels. |
| Approach: | They present the Cambridge Exams Publishing Open Cloze (CEPOC) dataset . they perform a set of experiments on three tasks: gap filling, gap prediction, and CEFR text classification. |
| Outcome: | The results of the study are promising for a number of NLP tasks. |
A New Dataset and Empirical Study for Sentence Simplification in Chinese (2023.acl-long)
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| Challenge: | Sentence simplification is a valuable technique that can benefit language learners and children. |
| Approach: | They propose a dataset for assessing sentence simplification in Chinese using manual simplifications from human annotators. |
| Outcome: | The proposed dataset shows that Chinese sentences are more accessible to children and nonnative readers than English sentences. |
Grammar Control in Dialogue Response Generation for Language Learning Chatbots (2025.naacl-long)
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| Challenge: | Existing language learning chatbots and research on second language acquisition benefit from these affordances. |
| Approach: | They ground a dialogue response generation model in a pedagogical repository of grammar skills and evaluate prompting, fine-tuning, and decoding strategies for grammar-controlled dialogue response generators. |
| Outcome: | The proposed model outperforms GPT-3.5 when tolerating minor response quality losses and predicts grammar-controlled responses to support grammar acquisition adapted to learner proficiency. |
ProLex: A Benchmark for Language Proficiency-oriented Lexical Substitution (2024.findings-acl)
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| Challenge: | Lexical Substitution fails to consider substitutes of equal or higher proficiency than the target word. |
| Approach: | They propose a task to find appropriate substitutes for a given word in a context sentence but not those that are of equal or higher proficiency than the target. |
| Outcome: | The proposed model outperforms ChatGPT by an average of 3.2% in F-score and achieves comparable results with GPT-4 on ProLex. |
Tonguescape: Exploring Language Models Understanding of Vowel Articulation (2025.naacl-long)
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| Challenge: | a study shows that language models can explain vowel pronunciation based on tongue positions . a visual LM can explain the relationship between vowels and tongue positions, but it is unclear whether they align textual information with visual information. |
| Approach: | They created video and image datasets from MRI data to examine if LMs associate real tongue positions with vowel articulation. |
| Outcome: | The proposed model can explain vowel pronunciation and the correlation between vowels and tongue positions as textual knowledge. |
An SLA Corpus Annotated with Pedagogically Relevant Grammatical Structures (L18-1)
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| Challenge: | a study using a framework to evaluate a language learner's proficiency in a second language aims to examine the production of learners with pedagogically relevant grammatical structures . |
| Approach: | They annotated texts produced by language learners with grammatical structures . they found that learners from different proficiency levels use pedagogically relevant structures compared to those of already certified language learners . |
| Outcome: | The annotated resource SGATe analyzes texts produced by language learners with grammatical structures . structure evolution along levels and level in which they are used the most was studied . |
Image Description Dataset for Language Learners (2022.lrec-1)
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| Challenge: | Language learners are limited by the number of texts or speech they are asked to answer . automatic assessment of image descriptions requires a system that depends on both the learner's native language and the target language. |
| Approach: | They propose a dataset that consists of images, their descriptions, and assessment annotations . they propose 'automatic error correction' task that encodes multimodal information from a learner sentence with an image and accurately decodes a corrected sentence. |
| Outcome: | The proposed model can revise errors that cannot be revised without an image. |
Exploring Methods for Generating Feedback Comments for Writing Learning (2021.emnlp-main)
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| Challenge: | Existing methods for generating explanatory notes for language learners are inadequate . nagata et al. demonstrates that neural-retrieval-based methods can generate feedback comments for preposition use . |
| Approach: | They investigate three different methods for generating feedback comments for preposition use . grammatical and writing items can also be used to generate feedback comments . |
| Outcome: | The proposed methods outperform neural-retrieval-based methods in generating feedback comments for preposition use. |
Rethinking Annotation: Can Language Learners Contribute? (2023.acl-long)
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| Challenge: | Researchers have traditionally recruited native speakers to provide annotations for benchmark datasets, but there are languages for which recruiting native speakers is difficult. |
| Approach: | They recruit 36 language learners and provide two types of additional resources and perform mini-tests to measure their language proficiency. |
| Outcome: | The proposed method improves learners' language proficiency in terms of vocabulary and grammar. |
TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages (2020.lrec-1)
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| Challenge: | a crowdsourcing project aimed at language learners has created a paraphrase corpus for 73 languages . the corpus contains 1.9 million sentences, with 200 - 250 000 sentences per language . |
| Approach: | They propose to use a Tatoeba-based dataset to create a paraphrase corpus for 73 languages. |
| Outcome: | The proposed dataset contains 1.9 million sentences and 200 - 250 000 sentences per language. |
Developing NLP Tools with a New Corpus of Learner Spanish (2020.lrec-1)
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Sam Davidson, Aaron Yamada, Paloma Fernandez Mira, Agustina Carando, Claudia H. Sanchez Gutierrez, Kenji Sagae
| Challenge: | Currently, there is little research on the development of effective NLP tools for the L2 classroom. |
| Approach: | They propose to use an annotated corpus of Spanish learner text to analyze developmental patterns and to develop a grammatical error correction system for Spanish learners. |
| Outcome: | The proposed system is based on annotated learner corpus of Spanish learners and includes error annotations and corrected text. |
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) often output text at a native level of speech, making them difficult to use for contexts where end-users are not fully proficient. |
| Approach: | They propose a framework to control the difficulty level of text generated by Large Language Models for contexts where end-users are not fully proficient. |
| Outcome: | The proposed framework surpasses GPT-4 and other models at fraction of the cost. |
Low-Resource Grammatical Error Correction: Selective Data Augmentation with Round-Trip Machine Translation (2025.findings-acl)
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| Challenge: | Existing methods for grammatical error correction require large amounts of parallel training data. |
| Approach: | They propose to generate synthetic data through round-trip machine translation by generating a set of character-level errors using a technique known as SeLex-RT. |
| Outcome: | The proposed technique produces errors similar to those observed with language learners, but lacks gold-labeled training data. |
The ParCoLab Parallel Corpus and Its Extension to Four Regional Languages of France (2024.lrec-main)
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Dejan Stosic, Saša Marjanović, Delphine Bernhard, Myriam Bras, Laurent Kevers, Stella Retali-Medori, Marianne Vergez-Couret, Carole Werner
| Challenge: | Parallel corpora are scarce for most of the world's language pairs. |
| Approach: | They propose to extend ParCoLab with a parallel corpus for Alsatian, Corsican, Occitan and Poitevin-Saintongeais. |
| Outcome: | The proposed corpus contains more than 20k tokens per regional language. |