Large language models (LLMs) finetuned to follow human instruction have recently exhibited significant capabilities in various English NLP tasks. However, their performance in grammatical error correction (GEC), especially on languages other than English, remains significantly unexplored. In this work, we evaluate the abilities of instruction finetuned LLMs in Arabic GEC, a complex task due to Arabic's rich morphology. Our findings suggest that various prompting methods, coupled with (in-context) few-shot learning, demonstrate considerable effectiveness, with GPT-4 achieving up to $65.49$ F$_{1}$ score under expert prompting (approximately $5$ points higher than our established baseline). Despite these positive results, we find that instruction finetuned models, regardless of their size, are still outperformed by fully finetuned ones, even if they are significantly smaller in size. This disparity highlights substantial room for improvements for LLMs. Inspired by methods used in low-resource machine translation, we also develop a method exploiting synthetic data that significantly outperforms previous models on two standard Arabic benchmarks. Our best model achieves a new SOTA on Arabic GEC, with $73.29$ and $73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively, compared to peer-reviewed published baselines.
ArbESC+: Arabic Enhanced Edit Selection System Combination for Grammatical Error Correction Resolving conflict and improving system combination in Arabic GEC
Department of Computer Sciences, Faculty of Computing and Information Technology（计算机科学系，计算与信息技术学院）；King Abdulaziz University（国王阿卜杜勒阿齐兹大学）；Saudi Electronic University（沙特电子大学）
Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model
King Abdulaziz University（阿卜杜勒阿齐兹大学）；Saudi Electronic University（沙特电子大学）
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study
Computational Approaches to Modeling Language Lab（语言建模计算方法实验室）；New York University Abu Dhabi（纽约大学阿布扎克分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Large Language Model-Driven Dynamic Assessment of Grammatical Accuracy in English Language Learner Writing
National Institute of Standards（国家标准 institute）；Department of Physics, Colorado State University（物理系，科罗拉多州立大学）；Electrical Engineering Department, University of Colorado（电气工程系，科罗拉多大学）
Language models align with human judgments on key grammatical constructions
Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学坎普ner自然与人工智能研究所）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Georgia Tech（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Paris Descartes University（巴黎笛卡尔大学）；University of Paris 13（巴黎第十三大学）
Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of…
Computational Approaches to Modeling Language (CAMeL) Lab（计算语言建模实验室）；New York University Abu Dhabi（纽约大学阿布扎比分校）
We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic's morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA's usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengt…
Benha University（本哈大学）；Faculty of Engineering (Shoubra)（工程学院（舒布拉））；Electronics Research Institute（电子研究所）
This paper describes a computationally inexpensive and efficient generic summarization algorithm for Arabic texts. The algorithm belongs to extractive summarization family, which reduces the problem into representative sentences identification and extraction sub-problems. Important keyphrases of the document to be summarized are identified employing combinations of statistical and linguistic features. The sentence extraction algorithm exploits keyphrases as the primary attributes to rank a sentence. The present experimental work, demonstrates different techniques for achieving various summariz…
Siteimprove（Siteimprove公司）；University of Copenhagen（哥本哈根大学）；Alexandra Institute（亚历山德拉研究所）；King’s College London（伦敦国王学院）；Imperial College London（伦敦帝国理工学院）
Evaluation of grammatical error correction (GEC) systems has primarily focused on essays written by non-native learners of English, which however is only part of the full spectrum of GEC applications. We aim to broaden the target domain of GEC and release CWEB, a new benchmark for GEC consisting of website text generated by English speakers of varying levels of proficiency. Website data is a common and important domain that contains far fewer grammatical errors than learner essays, which we show presents a challenge to state-of-the-art GEC systems. We demonstrate that a factor behind this is t…
Qatar Computing Research Institute（卡塔尔计算研究所）；MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Qatar Living（卡塔尔生活）
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on "Answer Selection in cQA", which included two subtasks: (a) classifying answers as "good", "bad", or "potentially relevant" with respect to the question, and (b) answering a YES/NO question with "yes", "no", or "unsure", based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQ…
Synthetic data generation is widely known to boost the accuracy of neural grammatical error correction (GEC) systems, but existing methods often lack diversity or are too simplistic to generate the broad range of grammatical errors made by human writers. In this work, we use error type tags from automatic annotation tools such as ERRANT to guide synthetic data generation. We compare several models that can produce an ungrammatical sentence given a clean sentence and an error type tag. We use these models to build a new, large synthetic pre-training data set with error tag frequency distributio…
This paper presents a simple recipe to train state-of-the-art multilingual Grammatical Error Correction (GEC) models. We achieve this by first proposing a language-agnostic method to generate a large number of synthetic examples. The second ingredient is to use large-scale multilingual language models (up to 11B parameters). Once fine-tuned on language-specific supervised sets we surpass the previous state-of-the-art results on GEC benchmarks in four languages: English, Czech, German and Russian. Having established a new set of baselines for GEC, we make our results easily reproducible and acc…
École Polytechnique（巴黎综合理工学院）；Université Sorbonne Paris Nord（巴黎第十三大学（索邦巴黎北大学））；New York University Abu Dhabi（纽约大学阿布扎比分校）；AUEB（雅典经济与商业大学）
Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequence-to-sequence architectures that are pretrained on large corpora. While most existing models focused on English, Arabic remained understudied. In this paper we propose AraBART, the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART. We show that AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based model and multil…
East China Normal University（华东师范大学）；Microsoft Research Asia（微软亚洲研究院）
We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with les…
Department of Electrical and Electronics Engineering（电气与电子工程系）
Fine-tuning a pretrained BERT model is the state of the art method for extractive/abstractive text summarization, in this paper we showcase how this fine-tuning method can be applied to the Arabic language to both construct the first documented model for abstractive Arabic text summarization and show its performance in Arabic extractive summarization. Our model works with multilingual BERT (as Arabic language does not have a pretrained BERT of its own). We show its performance in English corpus first before applying it to Arabic corpora in both extractive and abstractive tasks.
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）；The Chinese University of Hong Kong（香港中文大学）
Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns. In this paper, we propose a generic and language-independent strategy for multilingual GEC, which can train a GEC system effectively for a new non-English language with only two easy-to-access resources: 1) a pretrained cross-lingual language model (PXLM) and 2) parallel translation data between English and the language. Our approach creates diverse parallel GEC data without any language-specifi…
Charles University（查理大学）；Faculty of Mathematics and Physics（数学与物理学院）
Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech. We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets. AKCES-GEC is published under CC BY-NC-SA 4.0 license at https://hdl.handle.net/11234/1-3057 and the…
Medical University of Vienna（维也纳医科大学）；Complexity Science Hub Vienna（维也纳复杂科学中心）；Santa Fe Institute（圣塔菲研究所）；IIASA（国际应用系统分析研究所）
Sentence formation is a highly structured, history-dependent, and sample-space reducing (SSR) process. While the first word in a sentence can be chosen from the entire vocabulary, typically, the freedom of choosing subsequent words gets more and more constrained by grammar and context, as the sentence progresses. This sample-space reducing property offers a natural explanation of Zipf's law in word frequencies, however, it fails to capture the structure of the word-to-word transition probability matrices of English text. Here we adopt the view that grammatical constraints (such as subject--pre…
Princess Sumaya University for Technology（普林塞萨·苏马亚科技大学）；Hashemite University（哈希姆特大学）
Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to POS-tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this paper is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accurac…
Grammatical Error Correction (GEC) systems perform a sequence-to-sequence task, where an input word sequence containing grammatical errors, is corrected for these errors by the GEC system to output a grammatically correct word sequence. With the advent of deep learning methods, automated GEC systems have become increasingly popular. For example, GEC systems are often used on speech transcriptions of English learners as a form of assessment and feedback - these powerful GEC systems can be used to automatically measure an aspect of a candidate's fluency. The count of \textit{edits} from a candid…
New York University Abu Dhabi（纽约大学阿布扎比分校）；Carnegie Mellon University in Qatar（卡内基梅隆大学卡塔尔分校）
Gender bias in natural language processing (NLP) applications, particularly machine translation, has been receiving increasing attention. Much of the research on this issue has focused on mitigating gender bias in English NLP models and systems. Addressing the problem in poorly resourced, and/or morphologically rich languages has lagged behind, largely due to the lack of datasets and resources. In this paper, we introduce a new corpus for gender identification and rewriting in contexts involving one or two target users (I and/or You) -- first and second grammatical persons with independent gra…
In this research paper, I will elaborate on a method to evaluate machine translation models based on their performance on underlying syntactical phenomena between English and Arabic languages. This method is especially important as such "neural" and "machine learning" are hard to fine-tune and change. Thus, finding a way to evaluate them easily and diversely would greatly help the task of bettering them.
Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Stockholm University（斯德哥尔摩大学）
SERRANT is a system and code for automatic classification of English grammatical errors that combines SErCl and ERRANT. SERRANT uses ERRANT's annotations when they are informative and those provided by SErCl otherwise.
Baidu（百度）；Shanghai Pudong Development Bank（上海浦东发展银行）
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios. This paper describes the X-PuDu system that participated in SemEval-2022 Task 6, iSarcasmEval - Intended Sarcasm Detection in English and Arabic, which aims at detecting intended sarcasm in various settings of natural language understanding. Our solution finetunes pre-trained language models, such as ERNIE-M and DeBERTa, under the multilingual settings to recognize the irony from Arabic and English texts. Our system ra…
