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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 651–700 of 1773 papers

TitleStatusHype
Context-Adaptive Document-Level Neural Machine Translation—0
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation—0
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning—0
CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation—0
Graph-to-Sequence Neural Machine Translation—0
Attention based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages -- A case of Sanskrit to Hindi—0
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation—0
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue—0
Context-Aware Smoothing for Neural Machine Translation—0
Guiding Neural Machine Translation with Retrieved Translation Pieces—0
Hallucinations in Neural Machine Translation—0
Hallucinations in neural machine translation—0
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models—0
Analysing concatenation approaches to document-level NMT in two different domains—0
Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation—0
Context in Neural Machine Translation: A Review of Models and Evaluations—0
HausaMT v1.0: Towards English-Hausa Neural Machine Translation—0
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques—0
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction—0
Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture—0
HI-CMLM: Improve CMLM with Hybrid Decoder Input—0
Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation—0
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation—0
Hierarchical Sequence to Sequence Voice Conversion with Limited Data—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions—0
Hindi-Marathi Cross Lingual Model—0
Hindi to English: Transformer-Based Neural Machine Translation—0
Code-Switching with Word Senses for Pretraining in Neural Machine Translation—0
How Does Pretraining Improve Discourse-Aware Translation?—0
Continual Learning in Multilingual NMT via Language-Specific Embeddings—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?—0
A Two-Stage Curriculum Training Framework for NMT—0
Exploiting Deep Representations for Neural Machine Translation—0
How Much Does Tokenization Affect Neural Machine Translation?—0
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus—0
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
Human Evaluation of English--Irish Transformer-Based NMT—0
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles—0
Human Evaluation of Neural Machine Translation: The Case of Deep Learning—0
Code-switching pre-training for neural machine translation—0
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation—0
HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task—0
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks—0
HwTscSU’s Submissions on WAT 2022 Shared Task—0
Hybrid Self-Attention Network for Machine Translation—0
Icelandic Parallel Abstracts Corpus—0
Ask Language Model to Clean Your Noisy Translation Data—0
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