Papers with text editing
Fact-based Text Editing (2020.acl-main)
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| Challenge: | Existing methods for fact-based text editing are not suitable for all situations. |
| Approach: | They propose a method for automatically generating a dataset with a draft text, revised text, and several facts represented in triples. |
| Outcome: | The proposed method outperforms the encoder-decoder approach on two datasets and shows that it conducts inference faster than the encoded-decoding approach. |
PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction (2023.emnlp-industry)
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| Challenge: | a new study examines email marketing performance by considering email content and metadata. |
| Approach: | They propose a model that incorporates semantic and structural information from email data to generate latent exemplars for email response prediction. |
| Outcome: | The proposed model outperforms baseline models on two real-world email datasets . it provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models. |
Embedding-based Scientific Literature Discovery in a Text Editor Application (2020.acl-demos)
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| Challenge: | Despite the availability of powerful search engines and text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. |
| Approach: | They propose to combine text editing and literature discovery in an interactive user interface with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings. |
| Outcome: | The proposed application combines text editing and literature discovery in an interactive user interface. |
Text Editing as Imitation Game (2022.findings-emnlp)
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| Challenge: | Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification. |
| Approach: | They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens. |
| Outcome: | The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness. |
Learning with Latent Language (N18-1)
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| Challenge: | Using the space of natural language strings as a parameter space is an effective way to capture natural task structure. |
| Approach: | They propose to use natural language as a parameter space for few-shot learning problems including classification, transduction and policy search. |
| Outcome: | The proposed model outperforms models with a linguistic parameterization on image classification, text editing, and reinforcement learning. |
PLD+: Accelerating LLM Inference by Leveraging Language Model Artifacts (2025.findings-naacl)
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| Challenge: | speculative decoding is a novel decoding paradigm for large language models . however, its use is limited by its computational resources and fine-tuning requirements . |
| Approach: | They propose a tuning-free approach that accelerates inference of large language models . they use draft and verify principle to accelerate inference process . |
| Outcome: | The proposed approach outperforms tuning-free approaches on input-guided tasks and outperformed state-of-the-art EAGLE on four of the tasks. |
CoEdIT: Text Editing by Task-Specific Instruction Tuning (2023.findings-emnlp)
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| Challenge: | We present a large language model for writing assistance that is fine-tuned on task-specific instructions. |
| Approach: | They propose a large language model that is fine-tuned on task-specific instructions and outputs the edited text. |
| Outcome: | The proposed model performs better than other state-of-the-art models on various editing benchmarks while being 60x smaller. |
Consistency Training with Virtual Adversarial Discrete Perturbation (2022.naacl-main)
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| Challenge: | Existing methods for regularizing a model are agnostic to the training model and may not be effective for perturbed inputs. |
| Approach: | They propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions by replacing tokens while keeping original semantics. |
| Outcome: | The proposed method outperforms baselines on semi-supervised text classification tasks and a robustness benchmark. |
Blank Language Models (2020.emnlp-main)
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| Challenge: | Existing approaches focus on adapting left-to-right language models for text infilling. |
| Approach: | They propose a model that generates sequences by dynamically creating and filling in blanks. |
| Outcome: | Experiments on style transfer and damaged ancient text restoration show that the proposed model outperforms baseline models in terms of accuracy and fluency. |
Non-autoregressive Text Editing with Copy-aware Latent Alignments (2023.emnlp-main)
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| Challenge: | Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. |
| Approach: | They propose a non-autoregressive text editing method that models the edit process with latent CTC alignments and introduces the copy operation into the edit space. |
| Outcome: | The proposed method outperforms existing Seq2Edit models and achieves similar or even better results than Seq1Edit with over 4 speedup. |
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)
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| Challenge: | In a well-connected world, translation is of everincreasing importance. |
| Approach: | They propose to use mid-air hand gestures in combination with the keyboard for editing in machine translation and post-editing workflows to improve quality. |
| Outcome: | The proposed prototype supports mid-air hand gestures for cursor placement, text selection, deletion, and reordering. |
Contextualized Semantic Distance between Highly Overlapped Texts (2023.findings-acl)
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| Challenge: | Conventional semantic metrics are based on word representations and are vulnerable to disturbance of overlapped components with similar representations. |
| Approach: | They propose a mask-and-predict strategy to evaluate the semantic distance between the overlapped sentences using words in the longest common sequence as neighboring words and use masked language modeling to predict their positions. |
| Outcome: | The proposed method outperforms the state-of-the-art in domain adaption by a huge margin. |
Enhancing Text Editing for Grammatical Error Correction: Arabic as a Case Study (2025.acl-long)
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| Challenge: | Text editing is a wellstudied problem for grammatical error correction (GEC) but it is not the most efficient for morphologically rich languages like Arabic. |
| Approach: | They propose a text editing approach that derives edit tags directly from data, eliminating the need for language-specific edits. |
| Outcome: | The proposed approach achieves SOTA results on Arabic and performs on par with SOTA on two other languages. |
EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models (2025.acl-long)
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| Challenge: | Existing methods for text editing have been proposed for various types of data with diverse attributes. |
| Approach: | They propose a novel text editing method that modifies reference text to desired attributes at various scales. |
| Outcome: | The proposed method is capable of making precise adjustments within the desired range while maintaining the accuracy of the reference text. |
Rationalize and Align: Enhancing Writing Assistance with Rationale via Self-Training for Improved Alignment (2025.findings-acl)
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| Challenge: | Existing writing assistants rely on supervised fine-tuning to optimize models for multiple revisions. |
| Approach: | They propose a framework that enhances WA performance with rationale and alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art WAs and the closed-source GPT-4o by 3.9 and 7.1 points on average across eight well-established writing-related test sets. |