12,634 research outputs found
Pushed and Non-pushed Speaking Tasks in an EAP Context: What Are the Benefits for Linguistic Processing and Accuracy?
This article reports on a mixed methods study investigating the effectiveness of pushed and non-pushed speaking tasks in a UK university setting with upper-intermediate students. Specifically, the study addressed a) if a pushed speaking task produced more language related episodes (LREs) than a non-pushed speaking task b) the differences in the types of LREs produced by each task and c) whether a pushed speaking task resulted in more accurate usage of past narrative forms. Results showed that the pushed storytelling task produced significantly more LREs than the non-pushed task and it also identified that the most common LRE type for both pushed and non-pushed learners related to some form of output correction. The pushed group achieved greater accuracy gains from pretest and posttest scores but these gain scores were not found to be statistically significant. The study concludes that creating a push during spoken output activities can increase the occurrence of opportunities for linguistic processing, and subsequently interlanguage development, to occur
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
The sequence-to-sequence (Seq2Seq) approach has recently been widely used in
grammatical error correction (GEC) and shows promising performance. However,
the Seq2Seq GEC approach still suffers from two issues. First, a Seq2Seq GEC
model can only be trained on parallel data, which, in GEC task, is often noisy
and limited in quantity. Second, the decoder of a Seq2Seq GEC model lacks an
explicit awareness of the correctness of the token being generated. In this
paper, we propose a unified decoding intervention framework that employs an
external critic to assess the appropriateness of the token to be generated
incrementally, and then dynamically influence the choice of the next token. We
discover and investigate two types of critics: a pre-trained left-to-right
language model critic and an incremental target-side grammatical error detector
critic. Through extensive experiments on English and Chinese datasets, our
framework consistently outperforms strong baselines and achieves results
competitive with state-of-the-art methods.Comment: Accept to Findings of EMNLP 202
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
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting
Recent studies have revealed that grammatical error correction methods in the
sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply
utilizing adversarial examples in the pre-training or post-training process can
significantly enhance the robustness of GEC models to certain types of attack
without suffering too much performance loss on clean data. In this paper, we
further conduct a thorough robustness evaluation of cutting-edge GEC methods
for four different types of adversarial attacks and propose a simple yet very
effective Cycle Self-Augmenting (CSA) method accordingly. By leveraging the
augmenting data from the GEC models themselves in the post-training process and
introducing regularization data for cycle training, our proposed method can
effectively improve the model robustness of well-trained GEC models with only a
few more training epochs as an extra cost. More concretely, further training on
the regularization data can prevent the GEC models from over-fitting on
easy-to-learn samples and thus can improve the generalization capability and
robustness towards unseen data (adversarial noise/samples). Meanwhile, the
self-augmented data can provide more high-quality pseudo pairs to improve model
performance on the original testing data. Experiments on four benchmark
datasets and seven strong models indicate that our proposed training method can
significantly enhance the robustness of four types of attacks without using
purposely built adversarial examples in training. Evaluation results on clean
data further confirm that our proposed CSA method significantly improves the
performance of four baselines and yields nearly comparable results with other
state-of-the-art models. Our code is available at
https://github.com/ZetangForward/CSA-GEC
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