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 10501–10550 of 10752 papers

TitleStatusHype
Machine Translation for Multilingual Summary Content Evaluation—0
Offline Sentence Processing Measures for testing Readability with Users—0
Extraction de lexiques bilingues \`a partir de Wikip\'edia (Bilingual lexicon extraction from Wikipedia) [in French]—0
Processing Informal, Romanized Pakistani Text Messages—0
Graph-based alignment of narratives for automated neurological assessment—0
Exploring Grammatical Error Correction with Not-So-Crummy Machine Translation—0
Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words—0
Toward Determining the Comprehensibility of Machine Translations—0
Trait-Based Hypothesis Selection For Machine Translation—0
Using paraphrases for improving first story detection in news and Twitter—0
Using Senses in HMM Word Alignment—0
Tuning as Linear Regression—0
Transliteration Mining Using Large Training and Test Sets—0
Towards a Literary Machine Translation: The Role of Referential Cohesion—0
The Intelius Nickname Collection: Quantitative Analyses from Billions of Public Records—0
Why Not Grab a Free Lunch? Mining Large Corpora for Parallel Sentences to Improve Translation Modeling—0
Unified Expectation Maximization—0
Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure—0
Arabic Dialect Processing Tutorial—0
On-Demand Distributional Semantic Distance and Paraphrasing—0
Batch Tuning Strategies for Statistical Machine Translation—0
Implicitly Intersecting Weighted Automata using Dual Decomposition—0
A Pilot PropBank Annotation for Quranic Arabic—0
Low-Dimensional Discriminative Reranking—0
Re-examining Machine Translation Metrics for Paraphrase Identification—0
Automatic Parallel Fragment Extraction from Noisy Data—0
Deep Neural Network Language Models—0
Aligning Bilingual Literary Works: a Pilot Study—0
Every sensible extended top-down tree transducer is a multi bottom-up tree transducer—0
Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation—0
Revisiting the Case for Explicit Syntactic Information in Language Models—0
Structured Perceptron with Inexact Search—0
HyTER: Meaning-Equivalent Semantics for Translation Evaluation—0
Real-time Incremental Speech-to-Speech Translation of Dialogs—0
A Challenge Set for Advancing Language Modeling—0
Identifying Comparable Corpora Using LDA—0
Nudging the Envelope of Direct Transfer Methods for Multilingual Named Entity Recognition—0
Fast Inference in Phrase Extraction Models with Belief Propagation—0
Structured Ramp Loss Minimization for Machine Translation—0
Continuous Space Translation Models with Neural Networks—0
Digitizing 18th-Century French Literature: Comparing transcription methods for a critical edition text—0
Measuring the Influence of Long Range Dependencies with Neural Network Language Models—0
Are You Sure? Confidence in Prediction of Dependency Tree Edges—0
Fine-Grained Focus for Pinpointing Positive Implicit Meaning from Negated Statements—0
Insertion and Deletion Models for Statistical Machine Translation—0
Encouraging Consistent Translation Choices—0
Automatic Animacy Classification—0
Machine Translation of Arabic Dialects—0
Optimized Online Rank Learning for Machine Translation—0
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language ModelCode0
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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
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