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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 501–525 of 1773 papers

TitleStatusHype
EMMeTT: Efficient Multimodal Machine Translation Training—0
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC—0
A Comparison of Data Filtering Methods for Neural Machine Translation—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages—0
Capturing document context inside sentence-level neural machine translation models with self-training—0
Encoders Help You Disambiguate Word Senses in Neural Machine Translation—0
Encoding Gated Translation Memory into Neural Machine Translation—0
Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages—0
ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair—0
Conflict as an Inverse of Attention in Sequence Relationship—0
Adaptive Bridge between Training and Inference for Dialogue—0
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT—0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation—0
Conditional Variational Autoencoder for Neural Machine Translation—0
Concept Equalization to Guide Correct Training of Neural Machine Translation—0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Amortized Noisy Channel Neural Machine Translation—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
A Systematic Analysis of Vocabulary and BPE Settings for Optimal Fine-tuning of NMT: A Case Study of In-domain Translation—0
Compressing Neural Machine Translation Models with 4-bit Precision—0
A Mixed Learning Objective for Neural Machine Translation—0
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation—0
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