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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 201–250 of 1773 papers

TitleStatusHype
A Neural Approach to KGQA via SPARQL Silhouette Generation—0
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation—0
Combining Translation Memory with Neural Machine Translation—0
An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—0
Combining Sequence Distillation and Transfer Learning for Efficient Low-Resource Neural Machine Translation Models—0
Bi-Directional Neural Machine Translation with Synthetic Parallel Data—0
Bidirectional Generative Adversarial Networks for Neural Machine Translation—0
An empirical study on the effectiveness of images in Multimodal Neural Machine Translation—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Bi-Decoder Augmented Network for Neural Machine Translation—0
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain—0
Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish—0
Bilingual Methods for Adaptive Training Data Selection for Machine Translation—0
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation—0
A Deep Learning Based Approach to Transliteration—0
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation—0
A neural interlingua for multilingual machine translation—0
BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine Translation—0
Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation—0
Boosting Neural Machine Translation—0
Boosting Neural Machine Translation from Finnish to Northern Sámi with Rule-Based Backtranslation—0
Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian—0
Boosting Neural Networks to Decompile Optimized Binaries—0
BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis—0
An In-depth Walkthrough on Evolution of Neural Machine Translation—0
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training—0
Combining SMT and NMT Back-Translated Data for Efficient NMT—0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models—0
Bridging Neural Machine Translation and Bilingual Dictionaries—0
Combining Word and Character Vector Representation on Neural Machine Translation—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution—0
Bridging the Gap between Training and Inference for Neural Machine Translation—0
Building a Neural Machine Translation System Using Only Synthetic Parallel Data—0
Building a Parallel Corpus and Training Translation Models Between Luganda and English—0
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
A Parallel Corpus of Theses and Dissertations Abstracts—0
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation—0
Calibration of Encoder Decoder Models for Neural Machine Translation—0
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages—0
Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation—0
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?—0
Can Neural Machine Translation be Improved with User Feedback?—0
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation—0
Can Synthetic Translations Improve Bitext Quality?—0
Can Synthetic Translations Improve Bitext Quality?—0
Can the Variation of Model Weights be used as a Criterion for Self-Paced Multilingual NMT?—0
AR: Auto-Repair the Synthetic Data for Neural Machine Translation—0
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation—0
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