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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 Corpus for English-Japanese Multimodal Neural Machine Translation with Comparable Sentences—0
Combination of Neural Machine Translation Systems at WMT20—0
An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil—0
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—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
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation—0
Bi-Decoder Augmented Network for Neural Machine Translation—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation—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
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages—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
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network—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
Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian—0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models—0
Bridging Neural Machine Translation and Bilingual Dictionaries—0
Combining PBSMT and NMT Back-translated Data for Efficient NMT—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
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages—0
CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation—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
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity—0
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