NMT
489 papers with code • 0 benchmarks • 1 datasets
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.
Benchmarks
These leaderboards are used to track progress in NMT
Libraries
Use these libraries to find NMT models and implementations
MNIST
Most implemented papers
code2seq: Generating Sequences from Structured Representations of Code
tech-srl/code2seq
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ICLR 2019
The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval.
Neural Speech Synthesis with Transformer Network
Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the-art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs).
Masked Language Model Scoring
awslabs/mlm-scoring
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ACL 2020
Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one.
Language-agnostic BERT Sentence Embedding
FreddeFrallan/Multilingual-CLIP
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ACL 2022
While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.
Addressing the Rare Word Problem in Neural Machine Translation
atpaino/deep-text-corrector
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IJCNLP 2015
Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2. 8 BLEU points over an equivalent NMT system that does not use this technique.
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
surafelml/improving-zeroshot-nmt
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TACL 2017
In addition to improving the translation quality of language pairs that the model was trained with, our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation.
OpenNMT: Open-Source Toolkit for Neural Machine Translation
We describe an open-source toolkit for neural machine translation (NMT).
Towards Neural Phrase-based Machine Translation
In this paper, we present Neural Phrase-based Machine Translation (NPMT).
Very Deep Transformers for Neural Machine Translation
We explore the application of very deep Transformer models for Neural Machine Translation (NMT).
Modeling Coverage for Neural Machine Translation
Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate.
