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 less than 50% time cost for inference.
NUS Graduate School for Integrative Sciences and Engineering（新加坡国立大学综合科学与工程研究生院）；National University of Singapore（新加坡国立大学）
Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural networ…
Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited. To tackle this challenge, we first generate erroneous versions of large unannotated corpora using a realistic noising function. The resulting parallel corpora are subsequently used to pre-train Transformer models. Then, by sequentially applying transfer learning, we adapt these models to the domain and style of the test set. Combined with a context-aware neural spellchecker, our system achieves competitive results in both restricted and low resource t…
We propose Seq2Edits, an open-vocabulary approach to sequence editing for natural language processing (NLP) tasks with a high degree of overlap between input and output texts. In this approach, each sequence-to-sequence transduction is represented as a sequence of edit operations, where each operation either replaces an entire source span with target tokens or keeps it unchanged. We evaluate our method on five NLP tasks (text normalization, sentence fusion, sentence splitting & rephrasing, text simplification, and grammatical error correction) and report competitive results across the board. F…
Chinese Grammatical Error Correction (CGEC) aims to generate a correct sentence from an erroneous sequence, where different kinds of errors are mixed. This paper divides the CGEC task into two steps, namely spelling error correction and grammatical error correction. Specifically, we propose a novel zero-shot approach for spelling error correction, which is simple but effective, obtaining a high precision to avoid error accumulation of the pipeline structure. To handle grammatical error correction, we design part-of-speech (POS) features and semantic class features to enhance the neural network…
Tokyo Metropolitan University（东京都立大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Tohoku University（东北大学）
This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC). The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC. For example, the distribution of the inputs to a GEC model can be considerably different (erroneous, clumsy, etc.) from that of the corpora used for pre-training MLMs; however, this issue is not addressed in …
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…
In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations. We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size. Our best ensemble achieves a new SOTA result with an $F_{0.5}$ score of 76.05 on BEA-2019 (test), even without pre-training on synthetic datasets. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, "…
University of Minnesota（明尼苏达大学）；University of Virginia（弗吉尼亚大学）；Grammarly
Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document. Most recent research has focused on understanding and classifying different types of edits in the iterative revision process from human-written text instead of building accurate and robust systems for iterative text revision. In this work, we aim to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans (where-to-edit) with their…
École Polytechnique de Montréal（蒙特利尔高等理工学院）；Universitat Pompeu Fabra（庞培法布拉大学）
Previous works demonstrated that Automatic Text Summarization (ATS) by sentences extraction may be improved using sentence compression. In this work we present a sentence compressions approach guided by level-sentence discourse segmentation and probabilistic language models (LM). The results presented here show that the proposed solution is able to generate coherent summaries with grammatical compressed sentences. The approach is simple enough to be transposed into other languages.
Multi-head Sequence Tagging Model for Grammatical Error Correction
University of Technology and Applied Sciences（科技与应用科学大学）；National University of Defense Technology（国防科技大学）；CVTE Research（视源股份研究院）；Dalian University of Technology（大连理工大学）
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Vector Institute of AI（Vector人工智能研究所）；University of Central Florida（中佛罗里达大学）；University of British Columbia（不列颠哥伦比亚大学）；Emory University（埃默里大学）；Invertible AI
Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection
Graduate School of Artificial Intelligence, POSTECH, South Korea（POSTECH人工智能研究生院）；ETH Zurich, ETH AI Center（苏黎世联邦理工学院人工智能中心）；Department of Computer Science and Engineering, POSTECH, South Korea（POSTECH计算机科学与工程系）
Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data, especially when the system is trained only on clean data. In this paper, we show that augmenting training data with sentences containing artificially-introduced grammatical errors can make the system more robust to such errors. In combination with an automatic grammar error correction system, we can recover 1.5 BLEU out of 2.4 BLEU lost due to grammatical errors. We also present a set of Spanish translations of the JFLEG grammar error correction corpus, which allows for testing NMT robustness to real…
How Large Language Models Get Stuck: Early structure with persistent errors
Department of Statistics（统计学系）；University of Connecticut, Storrs（康涅狄格大学斯托尔分校）；Department of Linguistics（语言学系）；Department of Psychological Sciences（心理学科学系）
Octanove Labs；University of Washington（华盛顿大学）；University of Tokyo（东京大学）；Allen Institute for AI（艾伦人工智能研究所）
We present GrammarTagger, an open-source grammar profiler which, given an input text, identifies grammatical features useful for language education. The model architecture enables it to learn from a small amount of texts annotated with spans and their labels, which 1) enables easier and more intuitive annotation, 2) supports overlapping spans, and 3) is less prone to error propagation, compared to complex hand-crafted rules defined on constituency/dependency parses. We show that we can bootstrap a grammar profiler model with $F_1 \approx 0.6$ from only a couple hundred sentences both in Englis…
Grammar Error Correction(GEC) mainly relies on the availability of high quality of large amount of synthetic parallel data of grammatically correct and erroneous sentence pairs. The quality of the synthetic data is evaluated on how well the GEC system performs when pre-trained using it. But this does not provide much insight into what are the necessary factors which define the quality of these data. So this work aims to introduce 3 metrics - reliability, diversity and distribution match to provide more insight into the quality of large-scale synthetic data generated for the GEC task, as well a…
Dartmouth College（达特茅斯学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Political polarization in the US is on the rise. This polarization negatively affects the public sphere by contributing to the creation of ideological echo chambers. In this paper, we focus on addressing one of the factors that contributes to this polarity, polarized media. We introduce a framework for depolarizing news articles. Given an article on a certain topic with a particular ideological slant (eg., liberal or conservative), the framework first detects polar language in the article and then generates a new article with the polar language replaced with neutral expressions. To detect pola…
State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary's essential information and form. We propose to use sentence-compression data to train the post-editing model to ta…
Evaluating Multilingual Sentence Embeddings for Translation Error Detection:An English--Greek Contrastive Study
Ionian University（爱奥尼亚大学）；Laboratory on Digital Libraries and Electronic Publishing（数字图书馆与电子出版实验室）；Centre for Literary Translation and Book Policy（文学翻译与图书政策中心）
University of Washington（华盛顿大学）；Allen Institute for AI（艾伦人工智能研究所）
Modern neural language models can produce remarkably fluent and grammatical text. So much, in fact, that recent work by Clark et al. (2021) has reported that conventional crowdsourcing can no longer reliably distinguish between machine-authored (GPT-3) and human-authored writing. As errors in machine generations become ever subtler and harder to spot, it poses a new challenge to the research community for robust machine text evaluation. We propose a new framework called Scarecrow for scrutinizing machine text via crowd annotation. To support the broad range of real machine errors that can be i…
