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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 851–900 of 1773 papers

TitleStatusHype
NusaMT-7B: Machine Translation for Low-Resource Indonesian Languages with Large Language Models—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena—0
Octanove Labs' Japanese-Chinese Open Domain Translation System—0
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation—0
Off-the-Shelf Unsupervised NMT—0
On Compositionality in Neural Machine Translation—0
One Sentence One Model for Neural Machine Translation—0
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities—0
On Instruction-Finetuning Neural Machine Translation Models—0
On Learning Meaningful Code Changes via Neural Machine Translation—0
On Leveraging the Visual Modality for Neural Machine Translation—0
Online Distilling from Checkpoints for Neural Machine Translation—0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation—0
On Machine Translation of User Reviews—0
On NMT Search Errors and Model Errors: Cat Got Your Tongue?—0
On Search Strategies for Document-Level Neural Machine Translation—0
On Synthetic Data for Back Translation—0
On Target Representation in Continuous-output Neural Machine Translation—0
On the differences between BERT and MT encoder spaces and how to address them in translation tasks—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On-the-Fly Fusion of Large Language Models and Machine Translation—0
Towards Inducing Document-Level Abilities in Standard Multilingual Neural Machine Translation Models—0
On the Language Coverage Bias for Neural Machine Translation—0
On the Linguistic Representational Power of Neural Machine Translation Models—0
On the Relation between Position Information and Sentence Length in Neural Machine Translation—0
On the Relationship between Neural Machine Translation and Word Alignment—0
On the Sparsity of Neural Machine Translation Models—0
On the Sub-Layer Functionalities of Transformer Decoder—0
On the use of BERT for Neural Machine Translation—0
On the Word Alignment from Neural Machine Translation—0
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks—0
On Zero-shot Cross-lingual Transfer of Multilingual Neural Machine Translation—0
OpenNMT: Open-source Toolkit for Neural Machine Translation—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
OPPO NMT System for IWSLT 2019—0
Optimizing Segmentation Granularity for Neural Machine Translation—0
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning—0
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning—0
OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media—0
Our Neural Machine Translation Systems for WAT 2019—0
Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation—0
Panlingua-KMI MT System for Similar Language Translation Task at WMT 2019—0
ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation—0
Parallel Attention Forcing for Machine Translation—0
Parallel Corpus Filtering Based on Fuzzy String Matching—0
Parallel sentences mining with transfer learning in an unsupervised setting—0
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts—0
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