| Challenge: | Recent approaches to local sequence transduction are based on the popular encoder-decoder model for sequence to sequence learning. |
| Approach: | They propose a parallel iterative edit model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). |
| Outcome: | The proposed model is faster and more accurate than the current encoder-decoder model for local sequence transduction tasks like translation and paraphrasing. |
Similar Papers
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction (2022.acl-long)
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| Challenge: | Currently, machine translation (MT) is the mainstream approach for GEC. |
| Approach: | They propose to ensemble Transformer-based encoders by majority votes on span-level edits . their best ensemble achieves a new SOTA result even without pre-training on synthetic datasets - "Troy-Blogs" and "Try-1BW". |
| Outcome: | The proposed model achieves a new SOTA result even without pre-training on synthetic datasets. |
Grammatical Error Correction as GAN-like Sequence Labeling (2021.findings-acl)
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| Challenge: | Traditional GEC models learn from sentences with fixed error rates . sequence labeling approaches suffer from a couple of key problems . |
| Approach: | They propose a GAN-like sequence labeling model with a grammatical error detector and a generator to correct grammamatical errors. |
| Outcome: | The proposed model improves the state-of-the-art in GEC and improves on benchmarks. |
Corpora Generation for Grammatical Error Correction (N19-1)
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| Challenge: | Grammatical Error Correction (GEC) is a computational task that requires large amounts of data to solve. |
| Approach: | They propose two approaches to generate large parallel datasets for GEC using publicly available Wikipedia edit histories using minimal filtration heuristics and round-trip translation through bridge languages. |
| Outcome: | The proposed methods yield similar sized parallel corpora with around 4B tokens and are far ahead of the state-of-the-art on the CoNLL ‘14 benchmark and the JFLEG task. |
Reducing Sequence Length by Predicting Edit Spans with Large Language Models (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention. |
| Approach: | They propose to predict edit spans for local sequence transduction tasks by predicting edit span with a position of the source text and corrected tokens. |
| Outcome: | The proposed method reduces the length of the target sequence and the computational cost for inference by as small as 21%. |
Improved grammatical error correction by ranking elementary edits (2022.emnlp-main)
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| Challenge: | a new study shows that grammatical error correction models are far from perfect for English . reranking allows for a better classification of edits, but it can be difficult for other languages . |
| Approach: | They propose a two-stage reranking method for grammatical error correction using a model as edit generator and a sequence labeling model as second step. |
| Outcome: | The proposed method surpasses existing methods on BEA 2019 English dataset by 2-3%. |
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction (2020.acl-main)
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| Challenge: | Existing methods for incorporating a masked language model into an EncDec model have potential drawbacks when applied to GEC. |
| Approach: | They propose to incorporate a pre-trained masked language model (MLM) into an encoder-decoder model for grammatical error correction. |
| Outcome: | The proposed method achieves state-of-the-art on BEA-2019 and CoNLL-2014 benchmarks. |
Chinese Grammatical Correction Using BERT-based Pre-trained Model (2020.aacl-main)
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| Challenge: | Recent studies have shown that pre-trained models improve performance on downstream tasks. |
| Approach: | They propose to incorporate a pre-trained model into an encoder-decoder model to improve the performance of Chinese grammatical error correction tasks. |
| Outcome: | The proposed method improves the performance of Chinese grammatical error correction tasks. |
An Extended Sequence Tagging Vocabulary for Grammatical Error Correction (2023.findings-eacl)
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| Challenge: | Current sequence-to-sequence and sequence-tagging approaches treat GEC as a machine-translation problem. |
| Approach: | They propose to introduce specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. |
| Outcome: | The proposed approach outperforms existing methods on the BEA benchmark. |
A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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| Challenge: | Modern approaches view the task of Grammatical Error Correction (GEC) as monolingual text-to-text rewriting and employ encoderdecoder neural architectures. |
| Approach: | They propose a language-agnostic method to generate a large number of synthetic examples and use large-scale multilingual language models to train state-of-the-art GEC models. |
| Outcome: | The proposed method surpasses state-of-the-art results on GEC benchmarks in English, Czech, German and Russian. |
GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding (2023.acl-long)
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| Challenge: | grammatical error correction is an important NLP task that is usually solved with autoregressive sequence-to-sequence models. |
| Approach: | They propose a non-autoregressive approach to grammatical error correction that decouples a permutation network and a decoder network that fills in specific tokens. |
| Outcome: | The proposed approach improves over previously known non-autoregressive methods and reaches the level of autoregressive approaches that do not use language-specific synthetic data generation methods. |