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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 751–800 of 1773 papers

TitleStatusHype
Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads?—0
POS-Tagging based Neural Machine Translation System for European Languages using Transformers—0
Verdi: Quality Estimation and Error Detection for Bilingual CorporaCode0
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine TranslationCode0
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel DataCode0
Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation—0
Data Augmentation for Sign Language Gloss Translation—0
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters—0
Dynamic Multi-Branch Layers for On-Device Neural Machine TranslationCode0
Self-Guided Curriculum Learning for Neural Machine Translation—0
Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine TranslationCode0
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation—0
Addressing the Vulnerability of NMT in Input PerturbationsCode0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained EncodersCode0
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation—0
Sentence Alignment with Parallel Documents Facilitates Biomedical Machine TranslationCode0
Investigating Failures of Automatic Translation in the Case of Unambiguous Gender—0
Context-Adaptive Document-Level Neural Machine Translation—0
Simultaneous Multi-Pivot Neural Machine Translation—0
First the worst: Finding better gender translations during beam searchCode0
Sometimes We Want Translationese—0
Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey—0
The Curious Case of Hallucinations in Neural Machine TranslationCode0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
Sentiment-based Candidate Selection for NMTCode0
Better Neural Machine Translation by Extracting Linguistic Information from BERTCode0
Attention Forcing for Machine TranslationCode0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Domain Adaptation for NMT via Filtered Iterative Back-Translation—0
Measuring and Improving Faithfulness of Attention in Neural Machine Translation—0
MUCS@ - Machine Translation for Dravidian Languages using Stacked Long Short Term Memory—0
Towards Personalised and Document-level Machine Translation of Dialogue—0
Context-aware Neural Machine Translation with Mini-batch EmbeddingCode0
Complex Question Answering on knowledge graphs using machine translation and multi-task learning—0
Alignment verification to improve NMT translation towards highly inflectional languages with limited resources—0
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning—0
Why Find the Right One?—0
An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation—0
Leveraging Neural Machine Translation for Word AlignmentCode0
Contextual Text Embeddings for Twi—0
PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine TranslationCode0
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages—0
Repairing Pronouns in Translation with BERT-Based Post-Editing—0
Monolingual and Parallel Corpora for Kangri Low Resource LanguageCode0
Token-wise Curriculum Learning for Neural Machine Translation—0
Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language—0
Unsupervised Transfer Learning in Multilingual Neural Machine Translation with Cross-Lingual Word Embeddings—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
Self-Learning for Zero Shot Neural Machine Translation—0
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