Papers Document Level Machine Translation
“Document Level Machine Translation” 태그가 달린 논문 58편 · 필터 해제
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation
Document level Machine Translation (DocMT) approaches often struggle with effectively capturing discourse level phenomena. Existing approaches rely on heuristic rules to segment documents into discourse units, which rare…
Document Level Machine TranslationDocument TranslationLarge Language ModelMachine Translation+1Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
Document-level text generation tasks are known to be more difficult than sentence-level text generation tasks as they require the understanding of longer context to generate high-quality texts. In this paper, we investig…
Document Level Machine TranslationImage CaptioningMachine TranslationSentence+2Multilingual Contextualization of Large Language Models for Document-Level Machine Translation
Large language models (LLMs) have demonstrated strong performance in sentence-level machine translation, but scaling to document-level translation remains challenging, particularly in modeling long-range dependencies and…
Document Level Machine TranslationDocument TranslationMachine TranslationSentence+1DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced by noise from automatic speech recognitio…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Document Level Machine TranslationLanguage Modeling+7Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling document-level machine translation, by leve…
Document Level Machine TranslationMachine TranslationTranslationDOLFIN -- Document-Level Financial test set for Machine Translation
Despite the strong research interest in document-level Machine Translation (MT), the test sets dedicated to this task are still scarce. The existing test sets mainly cover topics from the general domain and fall short on…
Document Level Machine TranslationMachine TranslationTranslationDoc-Guided Sent2Sent++: A Sent2Sent++ Agent with Doc-Guided memory for Document-level Machine Translation
The field of artificial intelligence has witnessed significant advancements in natural language processing, largely attributed to the capabilities of Large Language Models (LLMs). These models form the backbone of Agents…
Document Level Machine TranslationMachine TranslationSentenceTranslationInvestigating Length Issues in Document-level Machine Translation
Transformer architectures are increasingly effective at processing and generating very long chunks of texts, opening new perspectives for document-level machine translation (MT). In this work, we challenge the ability of…
Document Level Machine TranslationMachine TranslationSentenceTranslationFine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation
Large language models (LLMs) have excelled in various NLP tasks, including machine translation (MT), yet most studies focus on sentence-level translation. This work investigates the inherent capability of instruction-tun…
Document Level Machine TranslationMachine TranslationSentenceTranslationM3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine Translation
Document translation poses a challenge for Neural Machine Translation (NMT) systems. Most document-level NMT systems rely on meticulously curated sentence-level parallel data, assuming flawless extraction of text from do…
Document Level Machine TranslationDocument TranslationMachine TranslationNMT+4Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning
Large language models (LLMs) exhibit outstanding performance in machine translation via in-context learning. In contrast to sentence-level translation, document-level translation (DOCMT) by LLMs based on in-context learn…
Document Level Machine TranslationIn-Context LearningMachine TranslationSentence+1Adapting Large Language Models for Document-Level Machine Translation
Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larger ones after task-specific fine-tuning.…
Document Level Machine TranslationDomain GeneralizationMachine TranslationTranslationImproving Long Context Document-Level Machine Translation
Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many works h…
Document Level Machine TranslationMachine TranslationNMTSentence+1Non-Autoregressive Document-Level Machine Translation
Non-autoregressive translation (NAT) models achieve comparable performance and superior speed compared to auto-regressive translation (AT) models in the context of sentence-level machine translation (MT). However, their …
Document Level Machine TranslationMachine TranslationSentenceText Generation+1Target-Side Augmentation for Document-Level Machine Translation
Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we p…
Data AugmentationDocument Level Machine TranslationMachine TranslationTranslationDocument-Level Machine Translation with Large Language Models
Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level machine translation (MT) as a testbed, …
Document Level Machine TranslationMachine TranslationTranslationHanoiT: Enhancing Context-aware Translation via Selective Context
Context-aware neural machine translation aims to use the document-level context to improve translation quality. However, not all words in the context are helpful. The irrelevant or trivial words may bring some noise and …
DecoderDocument Level Machine TranslationMachine TranslationSentence+1Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models
Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this p…
Abstractive Text SummarizationCross-Lingual Abstractive SummarizationDocument Level Machine TranslationMachine Translation+2A Bilingual Parallel Corpus with Discourse Annotations
Machine translation (MT) has almost achieved human parity at sentence-level translation. In response, the MT community has, in part, shifted its focus to document-level translation. However, the development of document-l…
Document Level Machine TranslationMachine TranslationSentenceTranslationModeling Context With Linear Attention for Scalable Document-Level Translation
Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long do…
Document Level Machine TranslationDocument TranslationInductive BiasMachine Translation+2