Diversity-Driven Combination for Grammatical Error Correction
- URL: http://arxiv.org/abs/2110.15149v1
- Date: Thu, 28 Oct 2021 14:20:43 GMT
- Title: Diversity-Driven Combination for Grammatical Error Correction
- Authors: Wenjuan Han, Hwee Tou Ng
- Abstract summary: Grammatical error correction (GEC) is the task of detecting and correcting errors in a written text.
To achieve successful system combination, multiple component systems need to produce corrected sentences that are both diverse and of comparable quality.
We present Diversity-Driven Combination (DDC) for GEC, a system combination strategy that encourages diversity among component systems.
- Score: 30.63256303821261
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Grammatical error correction (GEC) is the task of detecting and correcting
errors in a written text. The idea of combining multiple system outputs has
been successfully used in GEC. To achieve successful system combination,
multiple component systems need to produce corrected sentences that are both
diverse and of comparable quality. However, most existing state-of-the-art GEC
approaches are based on similar sequence-to-sequence neural networks, so the
gains are limited from combining the outputs of component systems similar to
one another. In this paper, we present Diversity-Driven Combination (DDC) for
GEC, a system combination strategy that encourages diversity among component
systems. We evaluate our system combination strategy on the CoNLL-2014 shared
task and the BEA-2019 shared task. On both benchmarks, DDC achieves significant
performance gain with a small number of training examples and outperforms the
component systems by a large margin. Our source code is available at
https://github.com/nusnlp/gec-ddc.
Related papers
- Grammatical Error Correction for Code-Switched Sentences by Learners of English [5.653145656597412]
We conduct the first exploration into the use of Grammar Error Correction systems on CSW text.
We generate synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora.
We then investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints.
Our best model achieves an average increase of 1.57 $F_0.5$ across 3 CSW test sets without affecting the model's performance on a monolingual dataset.
arXiv Detail & Related papers (2024-04-18T20:05:30Z) - Minimum Bayes' Risk Decoding for System Combination of Grammatical Error
Correction Systems [3.722707313671672]
This paper examines Minimum Bayes' Risk (MBR) decoding for Grammatical Error Correction (GEC) systems.
We propose a novel MBR loss function directly linked to this form of criterion.
Experiments on three popular GEC datasets and with state-of-the-art GEC systems demonstrate the efficacy of the proposed approach.
arXiv Detail & Related papers (2023-09-12T18:51:10Z) - Hybrid Rule-Neural Coreference Resolution System based on Actor-Critic
Learning [53.73316523766183]
Coreference resolution systems need to tackle two main tasks.
One task is to detect all of the potential mentions, the other is to learn the linking of an antecedent for each possible mention.
We propose a hybrid rule-neural coreference resolution system based on actor-critic learning.
arXiv Detail & Related papers (2022-12-20T08:55:47Z) - CSynGEC: Incorporating Constituent-based Syntax for Grammatical Error
Correction with a Tailored GEC-Oriented Parser [22.942594068051488]
This work considers another mainstream syntax formalism, i.e. constituent-based syntax.
We first propose an extended constituent-based syntax scheme to accommodate errors in ungrammatical sentences.
Then, we automatically obtain constituency trees of ungrammatical sentences to train a GEC-oriented constituency.
arXiv Detail & Related papers (2022-11-15T14:11:39Z) - Deep Combinatorial Aggregation [58.78692706974121]
Deep ensemble is a simple and effective method that achieves state-of-the-art results for uncertainty-aware learning tasks.
In this work, we explore a generalization of deep ensemble called deep aggregation (DCA)
DCA creates multiple instances of network components and aggregates their combinations to produce diversified model proposals and predictions.
arXiv Detail & Related papers (2022-10-12T17:35:03Z) - Task-Oriented Sensing, Computation, and Communication Integration for
Multi-Device Edge AI [108.08079323459822]
This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC)
We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
arXiv Detail & Related papers (2022-07-03T06:57:07Z) - Two-pass Decoding and Cross-adaptation Based System Combination of
End-to-end Conformer and Hybrid TDNN ASR Systems [61.90743116707422]
This paper investigates multi-pass rescoring and cross adaptation based system combination approaches for hybrid TDNN and Conformer E2E ASR systems.
The best combined system obtained using multi-pass rescoring produced statistically significant word error rate (WER) reductions of 2.5% to 3.9% absolute (22.5% to 28.9% relative) over the stand alone Conformer system on the NIST Hub5'00, Rt03 and Rt02 evaluation data.
arXiv Detail & Related papers (2022-06-23T10:17:13Z) - A Unified Strategy for Multilingual Grammatical Error Correction with
Pre-trained Cross-Lingual Language Model [100.67378875773495]
We propose a generic and language-independent strategy for multilingual Grammatical Error Correction.
Our approach creates diverse parallel GEC data without any language-specific operations.
It achieves the state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian)
arXiv Detail & Related papers (2022-01-26T02:10:32Z) - System Combination for Grammatical Error Correction Based on Integer
Programming [26.817392377302014]
We propose a system combination method for grammatical error correction (GEC) based on nonlinear integer programming (IP)
Our method optimize a novel F score objective based on error types, and combines multiple end-to-end GEC systems.
Experiments of the IP approach on combining state-of-the-art standalone GEC systems show that the combined system outperforms all standalone systems.
arXiv Detail & Related papers (2021-11-02T10:08:46Z) - BAGUA: Scaling up Distributed Learning with System Relaxations [31.500494636704598]
BAGUA is a new communication framework for distributed data-parallel training.
Powered by the new system design, BAGUA has a great ability to implement and extend various state-of-the-art distributed learning algorithms.
In a production cluster with up to 16 machines, BAGUA can outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training time.
arXiv Detail & Related papers (2021-07-03T21:27:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.