Papers Automatic Post-Editing
“Automatic Post-Editing” 태그가 달린 논문 124편 · 필터 해제
Exploring the Importance of Source Text in Automatic Post-Editing for Context-Aware Machine Translation
Accurate translation requires document-level information, which is ignored by sentence-level machine translation. Recent work has demonstrated that document-level consistency can be improved with automatic post-editing (…
Automatic Post-EditingMachine TranslationSentenceTranslationAutomatic Post-Editing for Vietnamese
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic …
Automatic Post-EditingMachine TranslationSentenceTranslationAdaptation of Back-translation to Automatic Post-Editing for Synthetic Data Generation
Automatic Post-Editing (APE) aims to correct errors in the output of a given machine translation (MT) system. Although data-driven approaches have become prevalent also in the APE task as in many other NLP tasks, there h…
Automatic Post-EditingMachine TranslationSentenceSynthetic Data Generation+1Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing
This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese. The source English texts were extracted from the WebNLG corpus and automatically translated …
Automatic Post-EditingMachine TranslationTranslationFindings of the WMT 2020 Shared Task on Automatic Post-Editing
We present the results of the 6th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a “black-box” machine translation system by learning from existing human c…
Automatic Post-EditingMachine TranslationNMTCross-Lingual Transformers for Neural Automatic Post-Editing
In this paper, we describe the Bering Lab’s submission to the WMT 2020 Shared Task on Automatic Post-Editing (APE). First, we propose a cross-lingual Transformer architecture that takes a concatenation of a source senten…
Automatic Post-EditingLanguage ModelingLanguage ModellingNMT+1POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model
This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs: English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cr…
Automatic Post-EditingLanguage ModelingLanguage ModellingMasked Language Modeling+1Noising Scheme for Data Augmentation in Automatic Post-Editing
This paper describes POSTECH’s submission to WMT20 for the shared task on Automatic Post-Editing (APE). Our focus is on increasing the quantity of available APE data to overcome the shortage of human-crafted training dat…
Automatic Post-EditingData AugmentationNMTAlibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT
The goal of Automatic Post-Editing (APE) is basically to examine the automatic methods for correcting translation errors generated by an unknown machine translation (MT) system. This paper describes Alibaba’s submissions…
Automatic Post-EditingBenchmarkingData AugmentationDecoder+4HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-tr…
Automatic Post-EditingNMTTranslationIncorporating Terminology Constraints in Automatic Post-Editing
Users of machine translation (MT) may want to ensure the use of specific lexical terminologies. While there exist techniques for incorporating terminology constraints during inference for MT, current APE approaches canno…
Automatic Post-EditingData AugmentationMachine TranslationTranslationMLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labels for up to 10,000 translations per lan…
ArticlesAutomatic Post-EditingMachine TranslationSentence+1Can Automatic Post-Editing Improve NMT?
Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine translation (SMT) systems but has not been as …
Automatic Post-EditingMachine TranslationNMTSpecificity+1Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators ne…
Automatic Post-EditingMachine TranslationTranslationDynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal machine translation, and the automatic post…
ArticlesAutomatic Post-EditingDocument SummarizationMachine Translation+2NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations
Machine Translation (MT) is one of the most important natural language processing applications. Independently of the applied MT approach, a MT system automatically generates an equivalent version (in some target language…
Automatic Post-EditingMachine TranslationNMTSentence+1A Post-Editing Dataset in the Legal Domain: Do we Underestimate Neural Machine Translation Quality?
We introduce a machine translation dataset for three pairs of languages in the legal domain with post-edited high-quality neural machine translation and independent human references. The data was collected as part of the…
Automatic Post-EditingMachine TranslationSentenceTranslationLearning Non-Monotonic Automatic Post-Editing of Translations from Human Orderings
Recent research in neural machine translation has explored flexible generation orders, as an alternative to left-to-right generation. However, training non-monotonic models brings a new complication: how to search for a …
Automatic Post-EditingMachine TranslationTranslationFelix: Flexible Text Editing Through Tagging and Insertion
We present Felix --- a flexible text-editing approach for generation, designed to derive the maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pre-training. In contrast to conven…
Automatic Post-EditingLanguage ModelingLanguage ModellingMachine Translation+4Learning to Copy for Automatic Post-Editing
Automatic post-editing (APE), which aims to correct errors in the output of machine translation systems in a post-processing step, is an important task in natural language processing. While recent work has achieved consi…
Automatic Post-EditingMachine TranslationTranslation