5 research outputs found
Neural Language Correction with Character-Based Attention
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
recurrent neural network with an attention mechanism. By operating at the
character level, the network avoids the problem of out-of-vocabulary words. We
illustrate the flexibility of our approach on dataset of noisy, user-generated
text collected from an English learner forum. When combined with a language
model, our method achieves a state-of-the-art -score on the CoNLL 2014
Shared Task. We further demonstrate that training the network on additional
data with synthesized errors can improve performance.Comment: 10 page
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting
We study sequence-to-sequence (seq2seq) pre-training with data augmentation
for sentence rewriting. Instead of training a seq2seq model with gold training
data and augmented data simultaneously, we separate them to train in different
phases: pre-training with the augmented data and fine-tuning with the gold
data. We also introduce multiple data augmentation methods to help model
pre-training for sentence rewriting. We evaluate our approach in two typical
well-defined sentence rewriting tasks: Grammatical Error Correction (GEC) and
Formality Style Transfer (FST). Experiments demonstrate our approach can better
utilize augmented data without hurting the model's trust in gold data and
further improve the model's performance with our proposed data augmentation
methods.
Our approach substantially advances the state-of-the-art results in
well-recognized sentence rewriting benchmarks over both GEC and FST.
Specifically, it pushes the CoNLL-2014 benchmark's score and JFLEG
Test GLEU score to 62.61 and 63.54 in the restricted training setting, 66.77
and 65.22 respectively in the unrestricted setting, and advances GYAFC
benchmark's BLEU to 74.24 (2.23 absolute improvement) in E&M domain and 77.97
(2.64 absolute improvement) in F&R domain
Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study
Neural sequence-to-sequence (seq2seq) approaches have proven to be successful
in grammatical error correction (GEC). Based on the seq2seq framework, we
propose a novel fluency boost learning and inference mechanism. Fluency
boosting learning generates diverse error-corrected sentence pairs during
training, enabling the error correction model to learn how to improve a
sentence's fluency from more instances, while fluency boosting inference allows
the model to correct a sentence incrementally with multiple inference steps.
Combining fluency boost learning and inference with convolutional seq2seq
models, our approach achieves the state-of-the-art performance: 75.72 (F_{0.5})
on CoNLL-2014 10 annotation dataset and 62.42 (GLEU) on JFLEG test set
respectively, becoming the first GEC system that reaches human-level
performance (72.58 for CoNLL and 62.37 for JFLEG) on both of the benchmarks.Comment: Substantial text overlap with "Fluency Boost Learning and Inference
for Neural Grammatical Error Correction" (accepted by ACL 2018
A Comprehensive Survey of Grammar Error Correction
Grammar error correction (GEC) is an important application aspect of natural
language processing techniques. The past decade has witnessed significant
progress achieved in GEC for the sake of increasing popularity of machine
learning and deep learning, especially in late 2010s when near human-level GEC
systems are available. However, there is no prior work focusing on the whole
recapitulation of the progress. We present the first survey in GEC for a
comprehensive retrospect of the literature in this area. We first give the
introduction of five public datasets, data annotation schema, two important
shared tasks and four standard evaluation metrics. More importantly, we discuss
four kinds of basic approaches, including statistical machine translation based
approach, neural machine translation based approach, classification based
approach and language model based approach, six commonly applied performance
boosting techniques for GEC systems and two data augmentation methods. Since
GEC is typically viewed as a sister task of machine translation, many GEC
systems are based on neural machine translation (NMT) approaches, where the
neural sequence-to-sequence model is applied. Similarly, some performance
boosting techniques are adapted from machine translation and are successfully
combined with GEC systems for enhancement on the final performance.
Furthermore, we conduct an analysis in level of basic approaches, performance
boosting techniques and integrated GEC systems based on their experiment
results respectively for more clear patterns and conclusions. Finally, we
discuss five prospective directions for future GEC researches
The UI System in the HOO 2012 Shared Task on Error Correction
We describe the University of Illinois (UI) system that participated in the Helping Our Own (HOO) 2012 shared task, which focuses on correcting preposition and determiner errors made by non-native English speakers. The task consisted of three metrics: Detection, Recognition, and Correction, and measured performance before and after additional revisions to the test data were made. Out of 14 teams that participated, our system scored first in Detection and Recognition and second in Correction before the revisions; and first in Detection and second in the other metrics after revisions. We describe our underlying approach, which relates to our previous work in this area, and propose an improvement to the earlier method, error inflation, which results in significant gains in performance.