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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 1651–1675 of 1773 papers

TitleStatusHype
Ensemble Learning for Multi-Source Neural Machine Translation—0
Ensemble of Translators with Automatic Selection of the Best Translation -- the submission of FOKUS to the WMT 18 biomedical translation task --—0
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation—0
ESPnet How2 Speech Translation System for IWSLT 2019: Pre-training, Knowledge Distillation, and Going Deeper—0
eTranslation’s Submissions to the WMT 2021 News Translation Task—0
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark—0
Evaluating Discourse Phenomena in Neural Machine Translation—0
Evaluating Explanation Methods for Neural Machine Translation—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages—0
Evaluating Robustness to Input Perturbations for Neural Machine Translation—0
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation—0
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems—0
Explicit Reordering for Neural Machine Translation—0
Exploiting Deep Representations for Neural Machine Translation—0
Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation—0
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques—0
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning—0
Exploiting Monolingual Data at Scale for Neural Machine Translation—0
Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learning—0
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation—0
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