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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 1001–1050 of 1773 papers

TitleStatusHype
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Content-Equivalent Translated Parallel News Corpus and Extension of Domain Adaptation for NMT—0
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language—0
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts—0
OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation—0
Multiword Expression aware Neural Machine Translation—0
FrameNet Annotations Alignment using Attention-based Machine Translation—0
Benchmarking Multidomain English-Indonesian Machine TranslationCode1
Evaluating Robustness to Input Perturbations for Neural Machine Translation—0
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot ParaphrasingCode1
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Vocabulary Adaptation for Distant Domain Adaptation in Neural Machine Translation—0
Syntax-aware Data Augmentation for Neural Machine Translation—0
Towards Reasonably-Sized Character-Level Transformer NMT by Finetuning Subword SystemsCode1
Revisiting Round-Trip Translation for Quality Estimation—0
Multiscale Collaborative Deep Models for Neural Machine TranslationCode0
Adversarial Subword Regularization for Robust Neural Machine TranslationCode1
Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models—0
Improving Massively Multilingual Neural Machine Translation and Zero-Shot TranslationCode1
Multiple Segmentations of Thai Sentences for Neural Machine Translation—0
When and Why is Unsupervised Neural Machine Translation Useless?—0
A Study of Non-autoregressive Model for Sequence Generation—0
Contextual Neural Machine Translation Improves Translation of Cataphoric PronounsCode0
Neural Machine Translation for Low-Resourced Indian LanguagesCode0
SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized EmbeddingsCode1
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models—0
Neural Machine Translation: Challenges, Progress and FutureCode1
An In-depth Walkthrough on Evolution of Neural Machine Translation—0
On Optimal Transformer Depth for Low-Resource Language TranslationCode0
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation ProblemCode1
Explicit Reordering for Neural Machine Translation—0
Dynamic Data Selection and Weighting for Iterative Back-TranslationCode0
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
Meta-Learning for Few-Shot NMT Adaptation—0
Understanding Learning Dynamics for Neural Machine Translation—0
Finding the Optimal Vocabulary Size for Neural Machine TranslationCode0
AR: Auto-Repair the Synthetic Data for Neural Machine Translation—0
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture—0
Low Resource Neural Machine Translation: A Benchmark for Five African LanguagesCode1
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation—0
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian PidginCode0
Towards Neural Machine Translation for Edoid Languages—0
Capturing document context inside sentence-level neural machine translation models with self-training—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Modeling Future Cost for Neural Machine Translation—0
Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation—0
Echo State Neural Machine Translation—0
MuST-Cinema: a Speech-to-Subtitles corpus—0
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue—0
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