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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 3601–3650 of 10752 papers

TitleStatusHype
Examining Large Pre-Trained Language Models for Machine Translation: What You Don't Know About It—0
Examining Scaling and Transfer of Language Model Architectures for Machine Translation—0
Examining the Relationship between Preordering and Word Order Freedom in Machine Translation—0
Approximate Distribution Matching for Sequence-to-Sequence Learning—0
Correlating decoding events with errors in Statistical Machine Translation—0
ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity—0
Expanding FLORES+ Benchmark for more Low-Resource Settings: Portuguese-Emakhuwa Machine Translation Evaluation—0
Expanding Parallel Resources for Medium-Density Languages for Free—0
Expanding the JHU Bible Corpus for Machine Translation of the Indigenous Languages of North America—0
Expanding wordnets to new languages with multilingual sense disambiguation—0
Expected Error Minimization with Ultraconservative Update for SMT—0
Experiences in Building the Let's MT! Portal on Amazon EC2—0
Experiences in Resource Generation for Machine Translation through Crowdsourcing—0
Experiences of Adapting Multimodal Machine Translation Techniques for Hindi—0
Experiments in Discriminating Similar Languages—0
Experiments in Medical Translation Shared Task at WMT 2014—0
A Concept Specification and Abstraction-based Semantic Representation: Addressing the Barriers to Rule-based Machine Translation—0
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Extending Phrase-Based Translation with Dependencies by Using Graphs—0
Experiments on Morphological Reinflection: CoNLL-2017 Shared Task—0
Experiments with Easy-first nonprojective constituent parsing—0
Experiments with Generative Models for Dependency Tree Linearization—0
Experiments with POS-based restructuring and alignment-based reordering for statistical machine translation—0
Experiments with Term Translation—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—0
Correction of Errors in Preference Ratings from Automated Metrics for Text Generation—0
A Multi-Task Architecture on Relevance-based Neural Query Translation—0
Explaining and Generalizing Back-Translation through Wake-Sleep—0
Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs—0
Extending the MuST-C Corpus for a Comparative Evaluation of Speech Translation Technology—0
Extending the Vocabulary of Fictional Languages using Neural Networks—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation—0
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation—0
Correcting Serial Grammatical Errors based on N-grams and Syntax—0
Explicit Reordering for Neural Machine Translation—0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
Generating English Determiners in Phrase-Based Translation with Synthetic Translation Options—0
Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages—0
Attempt Towards Stress Transfer in Speech-to-Speech Machine Translation—0
Case Studies in the Automatic Characterization of Grammars from Small Wordlists—0
Exploiting Bilingual Translation for Question Retrieval in Community-Based Question Answering—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
CASIA's System for IWSLT 2020 Open Domain Translation—0
CASICT-DCU Neural Machine Translation Systems for WMT17—0
Exploiting Deep Representations for Neural Machine Translation—0
Correcting Length Bias in Neural Machine Translation—0
Exploiting Language Models for Visual Recognition—0
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques—0
Correcting FLORES Evaluation Dataset for Four African Languages—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
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