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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
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction—0
Improving NMT Quality Using Terminology Injection—0
Improving NMT via Filtered Back Translation—0
Improving Robustness in Real-World Neural Machine Translation Engines—0
CUNI Transformer Neural MT System for WMT18—0
Improving Robustness of Retrieval Augmented Translation via Shuffling of Suggestions—0
Exploiting Deep Representations for Neural Machine Translation—0
Code-switching pre-training for neural machine translation—0
Improving the Robustness of Speech Translation—0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Ask Language Model to Clean Your Noisy Translation Data—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data—0
A Large-Scale Study of Machine Translation in the Turkic Languages—0
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation—0
Incorporating External Annotation to improve Named Entity Translation in NMT—0
Explicit Reordering for Neural Machine Translation—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
Incorporating Source-Side Phrase Structures into Neural Machine Translation—0
Incorporating Source Syntax into Transformer-Based Neural Machine Translation—0
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model—0
Incorporating translation quality estimation into Chinese-Korean neural machine translation—0
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring—0
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation—0
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems—0
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation—0
Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016—0
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inducing Grammars with and for Neural Machine Translation—0
Inference-only sub-character decomposition improves translation of unseen logographic characters—0
Information-Propogation-Enhanced Neural Machine Translation by Relation Model—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Insights from Gathering MT Productivity Metrics at Scale—0
Data Weighted Training Strategies for Grammatical Error Correction—0
Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation—0
Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission—0
Integrating Pre-trained Language Model into Neural Machine Translation—0
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages—0
Integrating Vectorized Lexical Constraints for Neural Machine Translation—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation—0
Interactive Attention for Neural Machine Translation—0
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation—0
Decoding and Diversity in Machine Translation—0
Introducing EM-FT for Manipuri-English Neural Machine Translation—0
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources—0
Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality Parallel Data Outperforms Traditional Web-Crawled Data—0
Exploring the Robustness of NMT Systems to Nonsensical Inputs—0
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021—0
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