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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6751–6800 of 10752 papers

TitleStatusHype
Extracting an English-Persian Parallel Corpus from Comparable Corpora—0
Extracting a Lexicon of Discourse Connectives in Czech from an Annotated Corpus—0
Arabic-English Text Translation Leveraging Hybrid NER—0
A Parallel Recurrent Neural Network for Language Modeling with POS Tags—0
BTG-based Machine Translation with Simple Reordering Model using Structured Perceptron—0
Unsupervised Bilingual Segmentation using MDL for Machine Translation—0
Between Reading Time and Information Structure—0
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation—0
Investigating Phrase-Based and Neural-Based Machine Translation on Low-Resource Settings—0
Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation—0
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification—0
Selective Decoding for Cross-lingual Open Information Extraction—0
CKY-based Convolutional Attention for Neural Machine Translation—0
Supervised Attention for Sequence-to-Sequence Constituency Parsing—0
XMU Neural Machine Translation Online Service—0
Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking—0
CADET: Computer Assisted Discovery Extraction and Translation—0
Neural Machine Translation: Basics, Practical Aspects and Recent Trends—0
Neural Lattice Search for Domain Adaptation in Machine Translation—0
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder—0
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification—0
Recall is the Proper Evaluation Metric for Word Segmentation—0
Lexical Simplification with the Deep Structured Similarity Model—0
Deriving Consensus for Multi-Parallel Corpora: an English Bible StudyCode0
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text PairsCode0
Key-value Attention Mechanism for Neural Machine Translation—0
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project—0
Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization—0
Substring Frequency Features for Segmentation of Japanese Katakana Words with Unlabeled Corpora—0
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information—0
Attentive Language Models—0
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels—0
Improving Japanese-to-English Neural Machine Translation by Voice Prediction—0
Context-Aware Smoothing for Neural Machine Translation—0
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems—0
Concept Equalization to Guide Correct Training of Neural Machine Translation—0
Geographical Evaluation of Word Embeddings—0
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task—0
SMT reranked NMT—0
A Bag of Useful Tricks for Practical Neural Machine Translation: Embedding Layer Initialization and Large Batch SizeCode0
Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017—0
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017—0
Building a Better Bitext for Structurally Different Languages through Self-training—0
MultiNews: A Web collection of an Aligned Multimodal and Multilingual Corpus—0
Tokyo Metropolitan University Neural Machine Translation System for WAT 2017—0
Overview of the 4th Workshop on Asian Translation—0
Kyoto University Participation to WAT 2017Code0
Japanese to English/Chinese/Korean Datasets for Translation Quality Estimation and Automatic Post-Editing—0
CUNI NMT System for WAT 2017 Translation Tasks—0
XMU Neural Machine Translation Systems for WAT 2017—0
Show:102550
← PrevPage 136 of 216Next →
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
5Transformer (ADMIN init)BLEU score43.8—Unverified
6AdminBLEU 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