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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 951–1000 of 1773 papers

TitleStatusHype
Fine-Grained Error Analysis on English-to-Japanese Machine Translation in the Medical Domain—0
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text—0
Finetuning a Kalaallisut-English machine translation system using web-crawled data—0
First Experiments with Neural Translation of Informal to Formal Mathematics—0
Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing—0
Fixing exposure bias with imitation learning needs powerful oracles—0
FJWU Participation for the WMT21 Biomedical Translation Task—0
Flow-Adapter Architecture for Unsupervised Machine Translation—0
Forest-Based Neural Machine Translation—0
FrameNet Annotations Alignment using Attention-based Machine Translation—0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
Frequency-Aware Contrastive Learning for Neural Machine Translation—0
From Balustrades to Pierre Vinken: Looking for Syntax in Transformer Self-Attentions—0
From LLM to NMT: Advancing Low-Resource Machine Translation with Claude—0
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model—0
Future-Prediction-Based Model for Neural Machine Translation—0
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages—0
Gender Aware Spoken Language Translation Applied to English-Arabic—0
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation—0
Gender-specific Machine Translation with Large Language Models—0
General2Specialized LLMs Translation for E-commerce—0
Generalization algorithm of multimodal pre-training model based on graph-text self-supervised training—0
Generalizing Back-Translation in Neural Machine Translation—0
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation—0
Generating Diverse Translation from Model Distribution with Dropout—0
Generating Gender Augmented Data for NLP—0
Getting Gender Right in Neural Machine Translation—0
GraphSeq2Seq: Graph-Sequence-to-Sequence for Neural Machine Translation—0
Graph-to-Sequence Neural Machine Translation—0
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue—0
Guiding Neural Machine Translation with Retrieved Translation Pieces—0
Hallucinations in Neural Machine Translation—0
Hallucinations in neural machine translation—0
Handling Homographs in Neural Machine Translation—0
Hard but Robust, Easy but Sensitive: How Encoder and Decoder Perform in Neural Machine Translation—0
HausaMT v1.0: Towards English-Hausa Neural Machine Translation—0
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction—0
HI-CMLM: Improve CMLM with Hybrid Decoder Input—0
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation—0
Hierarchical Sequence to Sequence Voice Conversion with Limited Data—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
HilMeMe: A Human-in-the-Loop Machine Translation Evaluation Metric Looking into Multi-Word Expressions—0
Hindi-Marathi Cross Lingual Model—0
Hindi to English: Transformer-Based Neural Machine Translation—0
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints—0
How Does Pretraining Improve Discourse-Aware Translation?—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?—0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures—0
How Much Does Tokenization Affect Neural Machine Translation?—0
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