| Challenge: | Seq2edit models decode only once without aware of subsequent tokens. |
| Approach: | They propose to iteratively refine the correction results of seq2seq models via Multi-Pass Decoding (MPD) to improve performance, but MPD increases inference costs . they propose to merge the source input and previous round correction result into one sequence. |
| Outcome: | Experiments on the CoNLL-14 and BEA-19 test set show that the proposed approach improves over baselines. |
Similar Papers
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study (P19-1)
Copied to clipboard
| Challenge: | Sequence-to-sequence (seq2sequ) models have a weakness: they cannot always generate sentences without grammatical errors. |
| Approach: | They propose to use automatic grammatical error correction to improve seq2seq models . they conduct experiments on machine translation, formality style transfer, sentence compression and simplification . |
| Outcome: | The proposed system can improve grammaticality of generated text and improve formal style tasks. |
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (P18-1)
Copied to clipboard
| Challenge: | Seq2seq models for grammatical error correction (GEC) have two limitations: (1) a seq2q model may not be well generalized with only limited error-corrected data; (2) a model may fail to completely correct a sentence with multiple errors through normal seq1sequeq inference. |
| Approach: | They propose a fluency boost learning and inference mechanism to improve the performance of seq2seq models for grammatical error correction (GEC) by generating fluency-boost sentence pairs during training. |
| Outcome: | Experiments show that the proposed model improves on both CoNLL-2014 and JFLEG benchmark datasets. |
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit. |
| Approach: | They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally. |
| Outcome: | The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets. |
TemplateGEC: Improving Grammatical Error Correction with Detection Template (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction (GEC) have been developed. |
| Approach: | They propose a method which integrates the detection labels from a Seq2Edit model to construct a template as the input. |
| Outcome: | The proposed method can perform human-in-the-loop error correction tasks. |
Efficient Grammatical Error Correction Via Multi-Task Training and Optimized Training Schedule (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent research has focused on using synthetic data for grammatical error correction . lack of annotated training data hinders progress in the field . |
| Approach: | They propose auxiliary tasks that exploit alignment between original and corrected sentences . they propose a sequence-to-sequence problem and perform multi-task training . |
| Outcome: | The proposed auxiliary tasks outperform the best models with a BART-based model on 11B parameters. |
Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for grammatical error correction are data-hungry and it is hard to train a seq2seq model with good performance without suf-Clean. |
| Approach: | They propose a method inspired by adversarial training to generate more meaningful and valuable training examples by continually identifying weak spots of a model and to enhance the model by gradually adding adversarials to the training set. |
| Outcome: | The proposed method improves generalization and robustness of GEC models by adding adversarial examples to the training set. |
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model (2020.aacl-main)
Copied to clipboard
| Challenge: | Strong pretraining approaches for grammatical error correction require extensive use of a pseudo-parallel corpus. |
| Approach: | They propose to use bidirectional and auto-regressive transformers as a generic pretrained encoder-decoder model for grammatical error correction (GEC) they find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC. |
| Outcome: | The proposed model achieves comparable results to the current strong results in English GEC. |
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction (2022.acl-short)
Copied to clipboard
| Challenge: | Modern writing assistance applications always contain a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences. |
| Approach: | They propose a simple yet effective approach to Align-and-Predict Decoding for most popular sequence-to-sequence models to offer more flexibility for the precision-recall trade-off. |
| Outcome: | The proposed model can be used in both English and Chinese GEC models and achieve state-of-the-art results. |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
Grammar-based Decoding for Improved Compositional Generalization in Semantic Parsing (2023.findings-acl)
Copied to clipboard
| Challenge: | Sequence-to-sequence (seq2sequ) models have been successful in semantic parsing tasks but struggle on out-of-distribution data. |
| Approach: | They propose to use a large-scale dialogue dataset to evaluate compositional generalization of semantic parsing. |
| Outcome: | The proposed model outperforms BART- and T5-based models on the SMCalflow-CS dataset on the zero-shot learning task. |