Papers with correction
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion (2021.acl-demo)
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| Challenge: | Existing computer-aided translation tools require the translator to edit incorrect parts of a document, while ITP tools require fewer edits. |
| Approach: | They propose an interactive translation interface with neural models that streamline the post-editing process on machine translation output. |
| Outcome: | The proposed interface can significantly improve translation quality and a user study shows that it speeds up the post-editing process by 52.9% compared to translating from scratch. |
Memo-SQL: Structured Decomposition and Experience-Driven Self-Correction for Training-Free NL2SQL (2026.findings-acl)
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| Challenge: | Existing NL2SQL systems rely on in-context learning with only correct examples . current test-time scaling methods often decompose questions arbitrarily, resulting in poor performance . |
| Approach: | They propose a structured decomposition and experience-aware self-correction framework for NL2SQL . they build a dynamic memory of successful queries and historical error–fix pairs . |
| Outcome: | The proposed framework achieves 68.5% execution accuracy on BIRD, setting new state of the art among open, zero-fine-tuning methods. |
Generating Commonsense Counterfactuals for Stable Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for relation extraction struggle to identify causal terms under the invariant entity constraint. |
| Approach: | They propose a framework to generate commonsense counterfactuals for stable relation extraction by using a knowledge base WordNet and a constituency parser. |
| Outcome: | The proposed framework significantly enhances the stability of relation extraction models. |
Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction (2024.lrec-main)
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| Challenge: | Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. |
| Approach: | They propose a method called controlled generation with Prompt Insertion that uses Large Language Models to explain the reasons for corrections in natural language. |
| Outcome: | The proposed method can explain the reasons for corrections in natural language by guiding the LLMs to generate explanations for all correction points. |
LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning (2025.acl-long)
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Weijie Shi, Han Zhu, Jiaming Ji, Mengze Li, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Sirui Han, Yike Guo
| Challenge: | Existing legal judgment prediction methods struggle with logical errors when conducting complex legal reasoning. |
| Approach: | They propose a method which enhances LJP reliability through step-wise verification and correction of the reasoning process. |
| Outcome: | The proposed model significantly improves concordance with court decisions from 72.37 to 80.27 on LLAMA-3.1-70B. |
Generation of a Spanish Artificial Collocation Error Corpus (L18-1)
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| Challenge: | collocations are combinations of two elements where one (the base) is freely chosen, despite the limitations of the other (collocate) current tools for collocation error detection and correction focus on collocation validation and identification of miscollocations . |
| Approach: | They propose an algorithm for automatic generation of an artificial collocation error corpus of american English learners of Spanish that includes 17 different types of collocation errors. |
| Outcome: | The proposed algorithm can detect and classify collocation errors in learners' writings . collocation error detection and correction has not received the attention it deserves . |
Chinese Spelling Corrector Is Just a Language Learner (2024.findings-acl)
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| Challenge: | a recent study shows that self-supervised learning can improve Chinese spelling correction by removing errors from training data. |
| Approach: | They propose a method that decodes Chinese spelling correction models using noise . they say it outperforms current methods that rely on annotated errors . |
| Outcome: | The proposed method outperforms the confusion set in specific domains because there are no errors in the training data. |
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier (2026.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enhanced capabilities in complex reasoning through step-by-step trace generation. |
| Approach: | They propose a generative verifier that dynamically allocates compute between rapid fast thinking and deliberative slow thinking. |
| Outcome: | The proposed solution outperforms GenPRM-32B on ProcessBench while requiring 2.3x fewer TFLOPS and 15x less training data. |