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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
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus—0
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation—0
Huawei's NMT Systems for the WMT 2019 Biomedical Translation Task—0
Human Evaluation of English--Irish Transformer-Based NMT—0
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles—0
Human Evaluation of Neural Machine Translation: The Case of Deep Learning—0
Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation—0
HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task—0
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks—0
HwTscSU’s Submissions on WAT 2022 Shared Task—0
Hybrid Self-Attention Network for Machine Translation—0
Icelandic Parallel Abstracts Corpus—0
Identifying and Controlling Important Neurons in Neural Machine Translation—0
Identifying and Controlling Important Neurons in Neural Machine Translation—0
Idiap NMT System for WAT 2019 Multimodal Translation Task—0
IIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for Indian languages—0
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task—0
IITP-MT at CALCS2021: English to Hinglish Neural Machine Translation using Unsupervised Synthetic Code-Mixed Parallel Corpus—0
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary—0
IITP-MT System for Gujarati-English News Translation Task at WMT 2019—0
Illinois Japanese English News Translation for WMT 2021—0
Implications of Multi-Word Expressions on English to Bharti Braille Machine Translation—0
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model—0
Improved English to Hindi Multimodal Neural Machine Translation—0
Improved English to Russian Translation by Neural Suffix Prediction—0
Improved Neural Machine Translation using Side Information—0
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations—0
Improvement in Machine Translation with Generative Adversarial Networks—0
Improving Autoregressive NLP Tasks via Modular Linearized Attention—0
Improving Autoregressive NMT with Non-Autoregressive Model—0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation—0
Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature—0
Improving Context-aware Neural Machine Translation with Target-side Context—0
Improving Document-Level Neural Machine Translation with Domain Adaptation—0
Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag—0
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language—0
Improving Long Context Document-Level Machine Translation—0
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation—0
Improving Machine Translation of Rare and Unseen Word Senses—0
Improving Machine Translation with Phrase Pair Injection and Corpus Filtering—0
Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax—0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation—0
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features—0
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning—0
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting—0
Improving Neural Machine Translation on resource-limited pairs using auxiliary data of a third language—0
Improving Neural Machine Translation through Phrase-based Forced Decoding—0
From Fully Trained to Fully Random Embeddings: Improving Neural Machine Translation with Compact Word Embedding Tables—0
Improving Neural Machine Translation with Neural Syntactic Distance—0
Improving Neural Machine Translation with Soft Template Prediction—0
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