SOTAVerified

Machine Translation

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

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: Google seq2seq )

Papers

Showing 72517300 of 10752 papers

TitleStatusHype
Chunk-Based Bi-Scale Decoder for Neural Machine TranslationCode0
STAIR Captions: Constructing a Large-Scale Japanese Image Caption DatasetCode0
Modeling Source Syntax for Neural Machine Translation0
Deep Neural Machine Translation with Linear Associative Unit0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation0
The Effect of Translationese on Tuning for Statistical Machine Translation0
North-S\'ami to Finnish rule-based machine translation system0
Normalizing Medieval German Texts: from rules to deep learning0
Mainstreaming August Strindberg with Text Normalization0
Machine translation with North Saami as a pivot language0
Joint UD Parsing of Norwegian Bokm and NynorskCode0
Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations0
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing0
Tilde MODEL - Multilingual Open Data for EU Languages0
Comparing Rule-based and SMT-based Spelling Normalisation for English Historical Texts0
Data Augmentation for Low-Resource Neural Machine TranslationCode1
Labelled network subgraphs reveal stylistic subtleties in written texts0
A GRU-Gated Attention Model for Neural Machine Translation0
Diversity driven Attention Model for Query-based Abstractive SummarizationCode0
Lexically Constrained Decoding for Sequence Generation Using Grid Beam SearchCode0
A Challenge Set Approach to Evaluating Machine Translation0
Neural Machine Translation via Binary Code Prediction0
Differentiable Scheduled Sampling for Credit Assignment0
Learning to Skim TextCode0
Translating NeuraleseCode0
Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine TranslationCode0
Neural System Combination for Machine Translation0
Bandit Structured Prediction for Neural Sequence-to-Sequence LearningCode0
A Semantic QA-Based Approach for Text Summarization Evaluation0
Adversarial Neural Machine Translation0
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification0
Baselines and test data for cross-lingual inferenceCode0
Does Neural Machine Translation Benefit from Larger Context?0
Sparse Communication for Distributed Gradient DescentCode1
Towards String-to-Tree Neural Machine Translation0
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation0
An entity-driven recursive neural network model for chinese discourse coherence modeling0
Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation0
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?0
Exploiting Cross-Sentence Context for Neural Machine TranslationCode0
Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy0
Learning Joint Multilingual Sentence Representations with Neural Machine Translation0
What do Neural Machine Translation Models Learn about Morphology?Code1
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation0
Unfolding and Shrinking Neural Machine Translation Ensembles0
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image ClassificationCode0
Online and Linear-Time Attention by Enforcing Monotonic AlignmentsCode0
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings0
Building a Neural Machine Translation System Using Only Synthetic Parallel Data0
Practical Neural Machine Translation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14Unverified
2Noisy back-translationBLEU score35Unverified
3Transformer+Rep(Uni)BLEU score33.89Unverified
4T5-11BBLEU score32.1Unverified
5BiBERTBLEU score31.26Unverified
6Transformer + R-DropBLEU score30.91Unverified
7Bi-SimCutBLEU score30.78Unverified
8BERT-fused NMTBLEU score30.75Unverified
9Data Diversification - TransformerBLEU score30.7Unverified
10SimCutBLEU score30.56Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4Unverified
2Noisy back-translationBLEU score45.6Unverified
3mRASP+Fine-TuneBLEU score44.3Unverified
4Transformer + R-DropBLEU score43.95Unverified
5Transformer (ADMIN init)BLEU score43.8Unverified
6AdminBLEU score43.8Unverified
7BERT-fused NMTBLEU score43.78Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5Unverified
9T5BLEU score43.4Unverified
10Local Joint Self-attentionBLEU score43.3Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43Unverified
2BiBERTBLEU score38.61Unverified
3Bi-SimCutBLEU score38.37Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96Unverified
5DRDABLEU score37.95Unverified
6Transformer + R-Drop + CutoffBLEU score37.9Unverified
7SimCutBLEU score37.81Unverified
8Cutoff+KneeBLEU score37.78Unverified
9CutoffBLEU score37.6Unverified
10CipherDAugBLEU score37.53Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97Unverified
2MS-COMET-22Score19.89Unverified
3MS-COMET-QE-22Score19.76Unverified
4KG-BERTScoreScore17.28Unverified
5metricx_xl_DA_2019Score17.17Unverified
6COMET-QEScore16.8Unverified
7COMET-22Score16.31Unverified
8UniTE-srcScore15.68Unverified
9UniTE-refScore15.38Unverified
10metricx_xxl_DA_2019Score15.24Unverified