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# Machine Translation Edit

405 papers with code · Natural Language Processing

Machine translation is the task of translating a sentence in a source language to a different target language.

# Unsupervised Pivot Translation for Distant Languages

6 Jun 2019

In this work, we introduce unsupervised pivot translation for distant languages, which translates a language to a distant language through multiple hops, and the unsupervised translation on each hop is relatively easier than the original direct translation.

# Bridging the Gap between Training and Inference for Neural Machine Translation

6 Jun 2019

Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.

# Robust Neural Machine Translation with Doubly Adversarial Inputs

6 Jun 2019

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations in the input.

# Imitation Learning for Non-Autoregressive Neural Machine Translation

5 Jun 2019

Non-autoregressive translation models (NAT) have achieved impressive inference speedup.

# Efficient, Lexicon-Free OCR using Deep Learning

5 Jun 2019

Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts.

# Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron

5 Jun 2019

We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data.

# Learning Deep Transformer Models for Machine Translation

5 Jun 2019

Transformer is the state-of-the-art model in recent machine translation evaluations.

# KERMIT: Generative Insertion-Based Modeling for Sequences

4 Jun 2019

During training, one can feed KERMIT paired data $(x, y)$ to learn the joint distribution $p(x, y)$, and optionally mix in unpaired data $x$ or $y$ to refine the marginals $p(x)$ or $p(y)$.

# Lattice-Based Transformer Encoder for Neural Machine Translation

4 Jun 2019

To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.

# Exploiting Sentential Context for Neural Machine Translation

4 Jun 2019

In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).