1,503 research outputs found
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturallyoccurring errors and attempt to introduce them into
other datasets. Initial work on inducing errors in this way using statistical
machine translation has shown promise; we investigate cheaply constructing
synthetic samples, given a small corpus of human-annotated data, using an
off-the-rack attentive sequence-to-sequence model and a straight-forward
post-processing procedure. Our approach yields error-filled artificial data
that helps a vanilla bi-directional LSTM to outperform the previous state of
the art at grammatical error detection, and a previously introduced model to
gain further improvements of over 5% score. When attempting to
determine if a given sentence is synthetic, a human annotator at best achieves
39.39 score, indicating that our model generates mostly human-like
instances.Comment: Accepted as a short paper at EMNLP 201
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction
Data Augmentation through generating pseudo data has been proven effective in
mitigating the challenge of data scarcity in the field of Grammatical Error
Correction (GEC). Various augmentation strategies have been widely explored,
most of which are motivated by two heuristics, i.e., increasing the
distribution similarity and diversity of pseudo data. However, the underlying
mechanism responsible for the effectiveness of these strategies remains poorly
understood. In this paper, we aim to clarify how data augmentation improves GEC
models. To this end, we introduce two interpretable and computationally
efficient measures: Affinity and Diversity. Our findings indicate that an
excellent GEC data augmentation strategy characterized by high Affinity and
appropriate Diversity can better improve the performance of GEC models. Based
on this observation, we propose MixEdit, a data augmentation approach that
strategically and dynamically augments realistic data, without requiring extra
monolingual corpora. To verify the correctness of our findings and the
effectiveness of the proposed MixEdit, we conduct experiments on mainstream
English and Chinese GEC datasets. The results show that MixEdit substantially
improves GEC models and is complementary to traditional data augmentation
methods.Comment: Accepted to Findings of EMNLP 202
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction
In Grammatical Error Correction (GEC), it is crucial to ensure the user's
comprehension of a reason for correction. Existing studies present tokens,
examples, and hints as to the basis for correction but do not directly explain
the reasons for corrections. Although methods that use Large Language Models
(LLMs) to provide direct explanations in natural language have been proposed
for various tasks, no such method exists for GEC. Generating explanations for
GEC corrections involves aligning input and output tokens, identifying
correction points, and presenting corresponding explanations consistently.
However, it is not straightforward to specify a complex format to generate
explanations, because explicit control of generation is difficult with prompts.
This study introduces a method called controlled generation with Prompt
Insertion (PI) so that LLMs can explain the reasons for corrections in natural
language. In PI, LLMs first correct the input text, and then we automatically
extract the correction points based on the rules. The extracted correction
points are sequentially inserted into the LLM's explanation output as prompts,
guiding the LLMs to generate explanations for the correction points. We also
create an Explainable GEC (XGEC) dataset of correction reasons by annotating
NUCLE, CoNLL2013, and CoNLL2014. Although generations from GPT-3 and ChatGPT
using original prompts miss some correction points, the generation control
using PI can explicitly guide to describe explanations for all correction
points, contributing to improved performance in generating correction reasons.Comment: Work in progres
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Large-scale pre-trained language models such as GPT-3 have shown remarkable
performance across various natural language processing tasks. However, applying
prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks
and their controllability remains underexplored. Controllability in GEC is
crucial for real-world applications, particularly in educational settings,
where the ability to tailor feedback according to learner levels and specific
error types can significantly enhance the learning process. This paper
investigates the performance and controllability of prompt-based methods with
GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact
of task instructions and examples on GPT-3's output, focusing on controlling
aspects such as minimal edits, fluency edits, and learner levels. Our findings
demonstrate that GPT-3 could effectively perform GEC tasks, outperforming
existing supervised and unsupervised approaches. We also showed that GPT-3
could achieve controllability when appropriate task instructions and examples
are given.Comment: Accepted in BEA 202
Grammatical Error Correction: A Survey of the State of the Art
Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of
grammatical errors, such as missing prepositions and mismatched subject-verb
agreement, but also orthographic and semantic errors, such as misspellings and
word choice errors respectively. The field has seen significant progress in the
last decade, motivated in part by a series of five shared tasks, which drove
the development of rule-based methods, statistical classifiers, statistical
machine translation, and finally neural machine translation systems which
represent the current dominant state of the art. In this survey paper, we
condense the field into a single article and first outline some of the
linguistic challenges of the task, introduce the most popular datasets that are
available to researchers (for both English and other languages), and summarise
the various methods and techniques that have been developed with a particular
focus on artificial error generation. We next describe the many different
approaches to evaluation as well as concerns surrounding metric reliability,
especially in relation to subjective human judgements, before concluding with
an overview of recent progress and suggestions for future work and remaining
challenges. We hope that this survey will serve as comprehensive resource for
researchers who are new to the field or who want to be kept apprised of recent
developments
Byte-Level Grammatical Error Correction Using Synthetic and Curated Corpora
Grammatical error correction (GEC) is the task of correcting typos, spelling,
punctuation and grammatical issues in text. Approaching the problem as a
sequence-to-sequence task, we compare the use of a common subword unit
vocabulary and byte-level encoding. Initial synthetic training data is created
using an error-generating pipeline, and used for finetuning two subword-level
models and one byte-level model. Models are then finetuned further on
hand-corrected error corpora, including texts written by children, university
students, dyslexic and second-language writers, and evaluated over different
error types and origins. We show that a byte-level model enables higher
correction quality than a subword approach, not only for simple spelling
errors, but also for more complex semantic, stylistic and grammatical issues.
In particular, initial training on synthetic corpora followed by finetuning on
a relatively small parallel corpus of real-world errors helps the byte-level
model correct a wide range of commonly occurring errors. Our experiments are
run for the Icelandic language but should hold for other similar languages,
particularly morphologically rich ones
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