Papers Automatic Post-Editing
“Automatic Post-Editing” 태그가 달린 논문 124편 · 필터 해제
Giving the Old a Fresh Spin: Quality Estimation-Assisted Constrained Decoding for Automatic Post-Editing
Automatic Post-Editing (APE) systems often struggle with over-correction, where unnecessary modifications are made to a translation, diverging from the principle of minimal editing. In this paper, we propose a novel tech…
Automatic Post-EditingBhashaVerse : Translation Ecosystem for Indian Subcontinent Languages
This paper focuses on developing translation models and related applications for 36 Indian languages, including Assamese, Awadhi, Bengali, Bhojpuri, Braj, Bodo, Dogri, English, Konkani, Gondi, Gujarati, Hindi, Hinglish, …
Automatic Post-EditingData AugmentationMachine TranslationTranslationTogether We Can: Multilingual Automatic Post-Editing for Low-Resource Languages
This exploratory study investigates the potential of multilingual Automatic Post-Editing (APE) systems to enhance the quality of machine translations for low-resource Indo-Aryan languages. Focusing on two closely related…
Automatic Post-EditingData AugmentationDomain AdaptationMulti-Task LearningHW-TSC's Submission to the CCMT 2024 Machine Translation Tasks
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024). We participate in the bilingual machine tran…
Automatic Post-EditingEnsemble LearningLanguage ModelingLanguage Modelling+4MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators
Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although approaches such as GEMBA-MQM have shown s…
Automatic Post-EditingMachine TranslationTranslationAPE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation
Automatic Post-Editing (APE) is the task of automatically identifying and correcting errors in the Machine Translation (MT) outputs. We propose a repair-filter-use methodology that uses an APE system to correct errors on…
Automatic Post-EditingMachine TranslationSentenceTranslationDomain Terminology Integration into Machine Translation: Leveraging Large Language Models
This paper discusses the methods that we used for our submissions to the WMT 2023 Terminology Shared Task for German-to-English (DE-EN), English-to-Czech (EN-CS), and Chinese-to-English (ZH-EN) language pairs. The task a…
Automatic Post-Editingde-enMachine TranslationTranslationTMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022
In this paper, we describe our TMU English–Japanese systems submitted to the restricted translation task at WAT 2022 (Nakazawa et al., 2022). In this task, we translate an input sentence with the constraint that certain …
Automatic Post-EditingMachine TranslationNMTSentence+1PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we…
Automatic Post-EditingMachine TranslationTranslationAn Empirical Study of Automatic Post-Editing
Automatic post-editing (APE) aims to reduce manual post-editing efforts by automatically correcting errors in machine-translated output. Due to the limited amount of human-annotated training data, data scarcity is one of…
Automatic Post-EditingData AugmentationMachine TranslationAutomatic Correction of Human Translations
We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-h…
Automatic Post-EditingTranslationEmpirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing
Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, consi…
Automatic Post-EditingMachine TranslationSynthetic Data GenerationTranslationAdvancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms
Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis m…
Automatic Post-EditingLanguage ModelingLanguage ModellingMachine Translation+1Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study
In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English. We first present a comparative stu…
Automatic Post-EditingLanguage ModelingLanguage ModellingA Self-Supervised Automatic Post-Editing Data Generation Tool
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, …
Automatic Post-EditingNetmarble AI Center’s WMT21 Automatic Post-Editing Shared Task Submission
This paper describes Netmarble’s submission to WMT21 Automatic Post-Editing (APE) Shared Task for the English-German language pair. First, we propose a Curriculum Training Strategy in training stages. Facebook Fair’s WMT…
Automatic Post-EditingMachine TranslationMulti-Task LearningTranslationAdapting Neural Machine Translation for Automatic Post-Editing
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT’21 Automatic Post-Editi…
Automatic Post-EditingMachine TranslationTranslationNetmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission
This paper describes Netmarble's submission to WMT21 Automatic Post-Editing (APE) Shared Task for the English-German language pair. First, we propose a Curriculum Training Strategy in training stages. Facebook Fair's WMT…
Automatic Post-EditingMachine TranslationMulti-Task LearningTranslationInteractive Models for Post-Editing
Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections. Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention.…
Automatic Post-EditingMachine TranslationTranslationTransfer Learning for Sequence Generation: from Single-source to Multi-source
Multi-source sequence generation (MSG) is an important kind of sequence generation tasks that takes multiple sources, including automatic post-editing, multi-source translation, multi-document summarization, etc. As MSG …
Automatic Post-EditingDocument SummarizationMulti-Document SummarizationTransfer Learning+1