SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–550 of 1773 papers

TitleStatusHype
EMMeTT: Efficient Multimodal Machine Translation Training—0
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC—0
Adaptive Bridge between Training and Inference for Dialogue—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages—0
Capturing document context inside sentence-level neural machine translation models with self-training—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Encoding Gated Translation Memory into Neural Machine Translation—0
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages—0
ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair—0
Factorized Transformer for Multi-Domain Neural Machine Translation—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation—0
Conditional Variational Autoencoder for Neural Machine Translation—0
Concept Equalization to Guide Correct Training of Neural Machine Translation—0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Amortized Noisy Channel Neural Machine Translation—0
Fast and Accurate Neural Machine Translation with Translation Memory—0
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
A Mixed Learning Objective for Neural Machine Translation—0
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation—0
Complex Question Answering on knowledge graphs using machine translation and multi-task learning—0
Factored Neural Machine Translation—0
Amharic-Arabic Neural Machine Translation—0
Asynchronous and Segmented Bidirectional Encoding for NMT—0
Adapting Multilingual NMT to Extremely Low Resource Languages FBK’s Participation in the Basque-English Low-Resource MT Task, IWSLT 2018—0
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words—0
Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017—0
Asymptotically Smaller Encodings for Graph Problems and Scheduling—0
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation—0
Factored Neural Machine Translation Architectures—0
Fast Domain Adaptation for Neural Machine Translation—0
Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content—0
Comparing Multilingual NMT Models and Pivoting—0
Comparing MT Approaches for Text Normalization—0
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario—0
A Survey on Low-Resource Neural Machine Translation—0
A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles—0
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation—0
Comparative Evaluation of NMT with Established SMT Programs—0
A Survey of Domain Adaptation for Neural Machine Translation—0
Communication-Efficient Federated Learning for Neural Machine Translation—0
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation—0
Domain Adaptive Inference for Neural Machine Translation—0
Combining Word and Character Vector Representation on Neural Machine Translation—0
All You Need is Source! A Study on Source-based Quality Estimation for Neural Machine Translation—0
Combining Translation Memory with Neural Machine Translation—0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
Show:102550
← PrevPage 11 of 36Next →

No leaderboard results yet.