| Challenge: | Generative modeling of editing text with respect to control attributes has seen increasing progress over the past few years. |
| Approach: | They propose an auxiliary text rewriting tool that facilitates the rewrite process for natural language generation tasks. |
| Outcome: | The proposed tool facilitates the rewriting process for natural language generation tasks, such as paraphrasing, text simplification, fairness-aware text rewrite, and text style transfer. |
Similar Papers
Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision) (2024.acl-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we focus on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria. |
| Approach: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks that takes a piece of text as input and generates a revision that is improved according to some specific criteria. |
| Outcome: | This tutorial focuses on text-to-text generation, a class of natural language generation tasks, that takes a piece of text as input and generates a revision that is improved according to some specificcriteria. |
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks (2022.emnlp-main)
Copied to clipboard
| Challenge: | Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness. |
| Approach: | They propose to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans with their corresponding edit intents. |
| Outcome: | The proposed system outperforms baselines on other text revision tasks and human evaluations. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Data-Driven Text Simplification (C18-3)
Copied to clipboard
| Challenge: | Automatic text simplification is the process of transforming a complex text into an equivalent version which would be easier to read or understand by automatic natural language processors. |
| Approach: | This tutorial provides an overview of automatic text simplification, which is the process of transforming a complex text into an equivalent version. |
| Outcome: | The aim of this paper is to provide a comprehensive overview of past and current research on automatic text simplification. |
Text Generation with Text-Editing Models (2022.naacl-tutorials)
Copied to clipboard
Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
| Challenge: | Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer. |
| Approach: | They propose to use text-editing models to predict edit operations applied to the source sequence and to generate outputs word-by-word from scratch. |
| Outcome: | This paper provides an overview of the text-edit based models and their current state-of-the-art approaches. |
Making Revisions Understandable: A Survey of Edit Intentions, Methods, and Applications (2026.findings-acl)
Copied to clipboard
| Challenge: | Text revision is a core process in document creation, capturing how authors iteratively refine, reorganize, and improve written content. |
| Approach: | They synthesize text revision research through the lens of edit intentions . they review prior work across the revision workflow including corpus construction, edit intention taxonomies, edit intentions, and edit intention identification. |
| Outcome: | The proposed approach synthesizes datasets, taxonomies, identification methods, and applications and highlights key open research directions. |
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for sentence simplification are supervised or unsupervised . paraphrasing captures complex edit operations, while revision-based methods provide more control and interpretability. |
| Approach: | They propose an unsupervised approach to sentence simplification that combines text generation and text revision. |
| Outcome: | The proposed method improves on the Newsela and ASSET datasets. |
Better Call SAUL: Fluent and Consistent Language Model Editing with Generation Regularization (2024.findings-emnlp)
Copied to clipboard
| Challenge: | State-of-the-art methods for updating large language models require computational overhead and lack theoretical validation. |
| Approach: | They propose a model editing method that uses sentence concatenation with augmented random facts for generation regularization. |
| Outcome: | The proposed method outperforms state-of-the-art methods while maintaining generation quality and reducing computational overhead. |
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction (2021.naacl-main)
Copied to clipboard
Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah
| Challenge: | Existing systems frame semantic parsing as a one-shot translation from a natural language question to the logical form. |
| Approach: | They propose a model that uses natural language feedback to correct parsers . they show that NL-EDIT can boost the accuracy of existing parser by 20% . |
| Outcome: | The proposed model can boost parsers' accuracy by 20% with just one turn of correction. |
DUnE: Dataset for Unified Editing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing models are susceptible to errors necessitating a comprehensive retraining process. |
| Approach: | They propose to define an edit as any natural language expression that solicits a change in the model’s outputs. |
| Outcome: | The proposed editing benchmarks show that retrieval-augmented language modeling outperforms specialized editing techniques and neither set of approaches has fully solved the generalized editing problem covered by the proposed benchmark. |