In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.
Nanjing University of Posts and Telecommunications（南京邮电大学）；Guangzhou University（广州大学）
ChatGPT is a publicly available chatbot that can quickly generate texts on given topics, but it is unknown whether the chatbot is really superior to human writers in all aspects of writing and whether its writing quality can be prominently improved on the basis of updating commands. Consequently, this study compared the writing performance on a narrative topic by ChatGPT and Chinese intermediate English (CIE) learners so as to reveal the chatbot's advantage and disadvantage in writing. The data were analyzed in terms of five discourse components using Coh-Metrix (a special instrument for analy…
Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing
Academic Center for Computing and Media Studies, Kyoto University, Japan（京都大学计算与媒体研究室）；College of Information Science and Engineering, Ritsumeikan University, Japan（立命馆大学信息科学与工程学院）
The ALTA Institute（ALTA研究所）；Computer Laboratory（计算机实验室）；University of Cambridge（剑桥大学）
We investigate the task of assessing sentence-level prompt relevance in learner essays. Various systems using word overlap, neural embeddings and neural compositional models are evaluated on two datasets of learner writing. We propose a new method for sentence-level similarity calculation, which learns to adjust the weights of pre-trained word embeddings for a specific task, achieving substantially higher accuracy compared to other relevant baselines.
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）
This paper reports on the recognition component of an intelligent tutoring system that is designed to help foreign language speakers learn standard English. The system models the grammar of the learner, with this instantiation of the system tailored to signers of American Sign Language (ASL). We discuss the theoretical motivations for the system, various difficulties that have been encountered in the implementation, as well as the methods we have used to overcome these problems. Our method of capturing ungrammaticalities involves using mal-rules (also called 'error productions'). However, the …
Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited knowledge about what inductive biases shape the way they generalize. We address that by investigating how popular seq2seq learners generalize in tasks that have high ambiguity in the training data. We use SCAN and three new tasks to study learners' preferences for memorization, arithmetic, hierarchical, and compositional reasoning. Further, we connect to Solomonoff's theory of induction and propose to use description length as a principled and sensitive measure of inductive biases. In our experimental study…
Assessing the validity of new paradigmatic complexity measures as criterial features for proficiency in L2 writings in English
Université Rennes 2（雷恩第二大学）；University of Galway（高威大学）；Universidad Internacional de la Rioja（拉里奥哈国际大学）；Université Paris Cité（巴黎西岱大学）；Université du Mans（勒芒大学）；Insight, Data Science Institute, University of Galway（高威大学洞察数据科学研究院）
Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication. In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. Whil…
In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners.
Chinese word usage errors often occur in non-native Chinese learners' writing. It is very helpful for non-native Chinese learners to detect them automatically when learning writing. In this paper, we propose a novel approach, which takes advantages of different auxiliary tasks, such as POS-tagging prediction and word log frequency prediction, to help the task of Chinese word usage error detection. With the help of these auxiliary tasks, we achieve the state-of-the-art results on the performances on the HSK corpus data, without any other extra data.
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization. Compositional data augmentation via example recombination has transferred some prior knowledge about compositionality to such black-box neural models for several semantic parsing tasks, but this often required task-specific engineering or provided limited gains. We present a more powerful data recombination method using a model called Compositional Structure Learner (CSL). CSL is a generative model with a quasi-synchronous context-free grammar backbone, which we induce from the…
Grammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC models mainly aim at improving accuracy, and their interpretability has not been explored. A promising approach for improving interpretability is an example-based method, which uses similar retrieved examples to generate corrections. In addition, examples are beneficial in language learning, helping learners understand the basis of grammatically incorrect/correct texts and improve their confidence in writing. Therefore, …
What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure
University of Southern Denmark（南丹麦大学）；Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）；Tel Aviv University（特拉维夫大学）；University of Glasgow（格拉斯哥大学）
Chinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings via three-level composition: (1) a convolutional neural network to extract the intra-character compositionality from the visual shape of a character; (2) a recurrent neural network with self-attention to compose character representation into word embeddings; (3) the Skip-Gram framework to capture non-compositionality directly from the contextual information. Evaluations demonstrate the superior performan…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Pompeu Fabra University（庞培法布拉大学）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team "nigula" obtained second place according to manual evaluation by the organizers.
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Beijing Language and Culture University（北京语言大学）；National University of Singapore（新加坡国立大学）
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC ev…
To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule. However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance. In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN). Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentag…
Annotating Errors in English Learners' Written Language Production: Advancing Automated Written Feedback Systems
Tohoku University, Sendai, Japan（东京东京大学）；RIKEN, Wako, Japan（日本理化学研究所）；ALTA Institute, Computer Laboratory, University of Cambridge, U.K（剑桥大学计算机实验室ALTA研究所）；INSA Lyon, Villeurbanne, France（里昂国立应用科学学院）；CNRS, LIS, Aix-Marseille University, Marseille, France（国家科学研究中心（CNRS））；MBZUAI, Abu Dhabi, United Arab Emirates（阿布扎赫德人工智能研究所）
We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objecti…
Natural language correction has the potential to help language learners improve their writing skills. While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing. On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models. Motivated by these issues, we present a neural network-based approach to language correction. The core component of our method is an encoder-decoder recurren…
University of Lisbon（里斯本大学）；University of Wolverhampton（伍尔弗汉普顿大学）；Harvard Medical School（哈佛医学院）；Macquarie University（麦考瑞大学）
In this paper we present NLI-PT, the first Portuguese dataset compiled for Native Language Identification (NLI), the task of identifying an author's first language based on their second language writing. The dataset includes 1,868 student essays written by learners of European Portuguese, native speakers of the following L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. NLI-PT includes the original student text and four different types of annotation: POS, fine-grained POS, constituency parses, and dependency…
LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity
Texas A&M University–Corpus Christi（德克萨斯A&M大学科珀斯克里斯蒂分校）；University of Missouri（密苏里大学）；University of California, Riverside（加利福尼亚大学河滨分校）；University of South Florida（南佛罗里达大学）
