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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 601–650 of 1773 papers

TitleStatusHype
Filtering Back-Translated Data in Unsupervised Neural Machine Translation—0
Comparing MT Approaches for Text Normalization—0
Exploiting Neural Query Translation into Cross Lingual Information Retrieval—0
Finding Sami Cognates with a Character-Based NMT Approach—0
Findings of the Fourth Workshop on Neural Generation and Translation—0
Findings of the WMT 2018 Shared Task on Automatic Post-Editing—0
Findings of the WMT 2020 Shared Task on Automatic Post-Editing—0
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation—0
Fine-Grained Attention Mechanism for Neural Machine Translation—0
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
Asynchronous and Segmented Bidirectional Encoding for NMT—0
Finetuning a Kalaallisut-English machine translation system using web-crawled data—0
Amharic-Arabic Neural Machine Translation—0
A Compact and Language-Sensitive Multilingual Translation Method—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
How Does Pretraining Improve Discourse-Aware Translation?—0
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain 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
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation—0
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?—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
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation—0
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning—0
A Comparison of Data Filtering Methods for Neural Machine Translation—0
Gender-specific Machine Translation with Large Language Models—0
General2Specialized LLMs Translation for E-commerce—0
Constraint Translation Candidates: A Bridge between Neural Query Translation and Cross-lingual Information Retrieval—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
Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20—0
Generating Diverse Translation from Model Distribution with Dropout—0
Generating Gender Augmented Data for NLP—0
Code Translation with Compiler Representations—0
Getting Gender Right in Neural Machine Translation—0
Exploiting Monolingual Data at Scale for Neural Machine Translation—0
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