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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 1551–1600 of 1773 papers

TitleStatusHype
Data Weighted Training Strategies for Grammatical Error Correction—0
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding—0
Debiasing Word Embeddings Improves Multimodal Machine Translation—0
Decoding and Diversity in Machine Translation—0
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation—0
Decoding with Value Networks for Neural Machine Translation—0
Deep Neural Machine Translation with Linear Associative Unit—0
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
Dependency-Based Relative Positional Encoding for Transformer NMT—0
Dependency-Based Self-Attention for Transformer NMT—0
Detecting Untranslated Content for Neural Machine Translation—0
Developing neural machine translation models for Hungarian-English—0
DFKI-NMT Submission to the WMT19 News Translation Task—0
DICTDIS: Dictionary Constrained Disambiguation for Improved NMT—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages—0
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages—0
Differences between SMT and NMT Output - a Translators' Point of View—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Direct Neural Machine Translation with Task-level Mixture of Experts models—0
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation—0
Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation—0
DiscreTalk: Text-to-Speech as a Machine Translation Problem—0
Dissecting Lottery Ticket Transformers: Structural and Behavioral Study of Sparse Neural Machine Translation—0
Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach—0
Diversity by Phonetics and its Application in Neural Machine Translation—0
Diving Deep into Context-Aware Neural Machine Translation—0
Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation—0
Document Graph for Neural Machine Translation—0
Document-level Neural Machine Translation with Document Embeddings—0
Document-level Neural Machine Translation with Associated Memory Network—0
Document-level Neural Machine Translation Using Dependency RST Structure—0
Document Level NMT of Low-Resource Languages with Backtranslation—0
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain—0
Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan—0
Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
Domain Adaptation for NMT via Filtered Iterative Back-Translation—0
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet—0
Domain Adaptation of Document-Level NMT in IWSLT19—0
Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020—0
Domain Adaptation of NMT models for English-Hindi Machine Translation Task : AdapMT Shared Task ICON 2020—0
Domain adapted machine translation: What does catastrophic forgetting forget and why?—0
Domain Control for Neural Machine Translation—0
Domain Informed Neural Machine Translation: Developing Translation Services for Healthcare Enterprise—0
Domain specialization: a post-training domain adaptation for Neural Machine Translation—0
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?—0
D-PAGE: Diverse Paraphrase Generation—0
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