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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
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary—0
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training—0
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation—0
Crafting Adversarial Examples for Neural Machine Translation—0
Continuous Language Generative FlowCode1
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation—0
On the differences between BERT and MT encoder spaces and how to address them in translation tasks—0
Fast and Accurate Neural Machine Translation with Translation Memory—0
Pre-training Methods for Neural Machine Translation—0
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation—0
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena—0
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective FeedbackCode1
Revisiting Negation in Neural Machine TranslationCode0
The USYD-JD Speech Translation System for IWSLT 2021—0
Modelling Latent Translations for Cross-Lingual TransferCode1
Modeling Bilingual Conversational Characteristics for Neural Chat TranslationCode1
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages—0
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical TranslationCode0
On the Copying Behaviors of Pre-Training for Neural Machine TranslationCode0
On the Difficulty of Translating Free-Order Case-Marking LanguagesCode0
Generating Gender Augmented Data for NLP—0
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoningCode0
A Survey on Low-Resource Neural Machine Translation—0
Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking—0
IITP at WAT 2021: System description for English-Hindi Multimodal Translation Task—0
NoDeeLe: A Novel Deep Learning Schema for Evaluating Neural Machine Translation Systems—0
Audiovisual Translation through NMT and Subtitling in the Netflix Series ‘Cable Girls’—0
MultiTraiNMT: Training Materials to Approach Neural Machine Translation from Scratch—0
Neural Machine Translation for Low-Resource Languages: A Survey—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space—0
Language Models are Good Translators—0
On the Evaluation of Machine Translation for Terminology ConsistencyCode1
Phrase-level Active Learning for Neural Machine Translation—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Revisiting the Weaknesses of Reinforcement Learning for Neural Machine TranslationCode1
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation—0
Sequence-Level Training for Non-Autoregressive Neural Machine TranslationCode1
Towards User-Driven Neural Machine TranslationCode0
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT—0
Self-supervised and Supervised Joint Training for Resource-rich Machine Translation—0
Translate, then Parse! A strong baseline for Cross-Lingual AMR ParsingCode0
On the Language Coverage Bias for Neural Machine Translation—0
Scalable Transformers for Neural Machine Translation—0
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine TranslationCode1
Restoring the Sister: Reconstructing a Lexicon from Sister Languages using Neural Machine TranslationCode0
The Helsinki submission to the AmericasNLP shared task—0
Using Confidential Data for Domain Adaptation of Neural Machine TranslationCode0
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