Papers Document Level Machine Translation
“Document Level Machine Translation” 태그가 달린 논문 58편 · 필터 해제
Document-Level Neural Machine Translation Using BERT as Context Encoder
Large-scale pre-trained representations such as BERT have been widely used in many natural language understanding tasks. The methods of incorporating BERT into document-level machine translation are still being explored.…
DecoderDocument Level Machine TranslationMachine TranslationNatural Language Understanding+2Improving NMT via Filtered Back Translation
Document-Level Machine Translation (MT) has become an active research area among the NLP community in recent years. Unlike sentence-level MT, which translates the sentences independently, document-level MT aims to utiliz…
Document Level Machine TranslationMachine TranslationNMTSentence+1Document-Level Machine Translation Evaluation Project: Methodology, Effort and Inter-Annotator Agreement
Document-level (doc-level) human eval-uation of machine translation (MT) has raised interest in the community after a fewattempts have disproved claims of “human parity” (Toral et al., 2018; Laubli et al.,2018). However,…
Document Level Machine TranslationMachine TranslationSentenceTranslationLeveraging Discourse Rewards for Document-Level Neural Machine Translation
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the d…
Document Level Machine TranslationMachine TranslationTranslationA Simple and Effective Unified Encoder for Document-Level Machine Translation
Most of the existing models for document-level machine translation adopt dual-encoder structures. The representation of the source sentences and the document-level contexts are modeled with two separate encoders. Althoug…
Document Level Machine TranslationMachine TranslationTranslationCorpora for Document-Level Neural Machine Translation
Instead of translating sentences in isolation, document-level machine translation aims to capture discourse dependencies across sentences by considering a document as a whole. In recent years, there have been more intere…
Document Level Machine TranslationMachine TranslationNMTSentence+1Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
Document-level machine translation incorporates inter-sentential dependencies into the translation of a source sentence. In this paper, we propose a new framework to model cross-sentence dependencies by training neural m…
Document Level Machine TranslationMachine TranslationNMTSentence+2Towards Making the Most of Context in Neural Machine Translation
Document-level machine translation manages to outperform sentence level models by a small margin, but have failed to be widely adopted. We argue that previous research did not make a clear use of the global context, and …
Document Level Machine TranslationMachine TranslationNMTSentence+1A Survey on Document-level Neural Machine Translation: Methods and Evaluation
Machine translation (MT) is an important task in natural language processing (NLP) as it automates the translation process and reduces the reliance on human translators. With the resurgence of neural networks, the transl…
Document Level Machine TranslationMachine TranslationTranslationHierarchical Modeling of Global Context for Document-Level Neural Machine Translation
Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context for translation. In this paper, we propose a hierarchical model to learn the global context for docu…
Document Level Machine TranslationMachine TranslationNMTSentence+1Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
Most neural machine translation systems still translate sentences in isolation. To make further progress, a promising line of research additionally considers the surrounding context in order to provide the model potentia…
Document Level Machine TranslationLanguage ModelingLanguage ModellingMachine Translation+2Better Document-Level Machine Translation with Bayes' Rule
We show that Bayes' rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents---a compelling benefit as parallel documents ar…
Document Level Machine TranslationDocument TranslationLanguage ModelingLanguage Modelling+3Using a Graph-based Coherence Model in Document-Level Machine Translation
Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation c…
Document Level Machine TranslationMachine TranslationText GenerationTranslationImproving Evaluation of Document-level Machine Translation Quality Estimation
Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable. In this paper, we explore the validity of human annot…
Document Level Machine TranslationMachine TranslationTranslationvalidUsing Coreference Links to Improve Spanish-to-English Machine Translation
In this paper, we present a proof-of-concept implementation of a coreference-aware decoder for document-level machine translation. We consider that better translations should have coreference links that are closer to tho…
Coreference ResolutionDecoderDiversityDocument Level Machine Translation+3