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

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Showing 6701–6750 of 10752 papers

TitleStatusHype
Discrete Autoencoders for Sequence ModelsCode0
Context Models for OOV Word Translation in Low-Resource Languages—0
A Resource-Light Method for Cross-Lingual Semantic Textual SimilarityCode0
Image Captioning using Deep Neural ArchitecturesCode0
Variational Recurrent Neural Machine Translation—0
What Level of Quality can Neural Machine Translation Attain on Literary Text?—0
Improved English to Russian Translation by Neural Suffix Prediction—0
DeepSeek: Content Based Image Search & Retrieval—0
Translating Pro-Drop Languages with Reconstruction ModelsCode0
MIZAN: A Large Persian-English Parallel CorpusCode0
Improving Wordnets for Under-Resourced Languages Using Machine Translation—0
Lexical Perspective on Wordnet to Wordnet Mapping—0
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation—0
Associative Conversation Model: Generating Visual Information from Textual Information—0
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks—0
A Hierarchical Model for Device Placement—0
Leveraging Orthographic Similarity for Multilingual Neural Transliteration—0
Leveraging Data Resources for Cross-Linguistic Information Retrieval Using Statistical Machine Translation—0
Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Generative Models for Alignment and Data Efficiency in Language—0
MaskGAN: Better Text Generation via Filling in the _______—0
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES—0
Tree2Tree Learning with Memory Unit—0
The Set Autoencoder: Unsupervised Representation Learning for Sets—0
A Flexible Approach to Automated RNN Architecture Generation—0
Low Resourced Machine Translation via Morpho-syntactic Modeling: The Case of Dialectal Arabic—0
DeepNorm-A Deep Learning Approach to Text Normalization—0
Sockeye: A Toolkit for Neural Machine TranslationCode0
A Berkeley View of Systems Challenges for AI—0
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits—0
Sequence to Sequence Networks for Roman-Urdu to Urdu Transliteration—0
Multi-channel Encoder for Neural Machine Translation—0
Distance-based Self-Attention Network for Natural Language Inference—0
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation—0
Neural Machine Translation by Generating Multiple Linguistic Factors—0
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks—0
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
Domain-independent Punctuation and Segmentation Insertion—0
CHARCUT: Human-Targeted Character-Based MT Evaluation with Loose DifferencesCode0
Survey: Multiword Expression Processing: A Survey—0
Learning from Parenthetical Sentences for Term Translation in Machine Translation—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding—0
Deep Learning Scaling is Predictable, Empirically—0
Decoding with Value Networks for Neural Machine Translation—0
Book Review: Syntax-Based Statistical Machine Translation by Philip Williams, Rico Sennrich, Matt Post and Philipp Koehn—0
IJCNLP-2017 Task 4: Customer Feedback Analysis—0
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017—0
Kyoto University MT System Description for IWSLT 2017—0
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All datasetsWMT2014 English-GermanWMT2014 English-FrenchIWSLT2014 German-EnglishACESWMT2016 English-RomanianWMT2016 Romanian-EnglishWMT2014 German-EnglishIWSLT2015 German-EnglishWMT2016 English-GermanIWSLT2015 English-VietnameseIWSLT2015 English-GermanWMT2016 German-English

Benchmark Results

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