| Challenge: | Using statistical learning, a computer can rephrase a sentence by only pointing at words that should be avoided. |
| Approach: | They propose a framework for computer-assisted text editing that relies on simple interactions between human editors and tokens. |
| Outcome: | The proposed framework allows to get substantial modifications to a sentence without human intervention. |
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Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (D18-1)
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| Challenge: | Existing approaches to inducing APE have suffered from over-correction, where the APE system tends to keep the machine translated text without any modification. |
| Approach: | They propose a neural programmer-interpreter approach to automated post-editing (APE) that mimics human perform post- editing using discrete edit operations . their model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
| Outcome: | The proposed model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
OpenTIPE: An Open-source Translation Framework for Interactive Post-Editing Research (2023.acl-demo)
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| Challenge: | Recent advances in machine translation have not yet improved translation quality . human post-editors must review and post- edit the output to ensure high-quality translations . current approaches do not consider the human interactions that occur in real post- editing scenarios. |
| Approach: | They propose a flexible and extensible framework that supports research on interactive post-editing. |
| Outcome: | The proposed framework aims to support research on interactive post-editing . it showcases its main functionalities with a demonstration video and an online live demo . |
Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020.aacl-main)
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| Challenge: | Existing methods for machine translation require intensive keyboard interaction, which is inconvenient on mobile devices. |
| Approach: | They propose a touch-based editing method that is more flexible than keyboard-mouse-based translation postediting. |
| Outcome: | The proposed method significantly outperforms existing interactive translation methods on translation datasets and on post-editing datasets. |
Simple and Effective Paraphrastic Similarity from Parallel Translations (P19-1)
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| Challenge: | Existing methods for learning paraphrastic sentence embeddings on bitext are expensive and require manual annotation. |
| Approach: | They propose a method that trains paraphrastic sentence embeddings directly from bitext, eliminating the time-consuming step of creating paraphrase corpora. |
| Outcome: | The proposed model outperforms and is faster than state-of-the-art models on cross-lingual tasks. |
Text Generation with Text-Editing Models (2022.naacl-tutorials)
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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. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
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. |
Learning to Copy for Automatic Post-Editing (D19-1)
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| Challenge: | Automatic post-editing (APE) is an important task in natural language processing. |
| Approach: | They propose a method that explicitly models how to copy words from a machine translation to a correct translation. |
| Outcome: | The proposed method outperforms all published methods on the WMT 2016-2017 datasets. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |