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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 3401–3450 of 10752 papers

TitleStatusHype
Enhancing Gender-Inclusive Machine Translation with Neomorphemes and Large Language Models—0
Enhancing Grammatical Cohesion: Generating Transitional Expressions for SMT—0
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation—0
Counterfactual Learning for Machine Translation: Degeneracies and Solutions—0
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss—0
Enhancing Language Learning through Technology: Introducing a New English-Azerbaijani (Arabic Script) Parallel Corpus—0
Enhancing Large Language Models'Machine Translation via Dynamic Focus Anchoring—0
Self-Enhancing Multi-filter Sequence-to-Sequence Model—0
Enhancing Lemmatization for Mongolian and its Application to Statistical Machine Translation—0
Enhancing Length Extrapolation in Sequential Models with Pointer-Augmented Neural Memory—0
Building a Hierarchically Aligned Chinese-English Parallel Treebank—0
Enhancing Machine Translation of Academic Course Catalogues with Terminological Resources—0
Enhancing Machine Translation through Advanced In-Context Learning: A Methodological Strategy for GPT-4 Improvement—0
Enhancing Neural Machine Translation of Low-Resource Languages: Corpus Development, Human Evaluation and Explainable AI Architectures—0
Enhancing Neural Machine Translation with Syntactic Ambiguities—0
Building a Monolingual Parallel Corpus for Text Simplification Using Sentence Similarity Based on Alignment between Word Embeddings—0
Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation—0
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network—0
Enhancing Root Extractors Using Light Stemmers—0
Building an Arabic Machine Translation Post-Edited Corpus: Guidelines and Annotation—0
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach—0
Enhancing Statistical Machine Translation with Character Alignment—0
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training—0
Enhancing the Nonlinear Mutual Dependencies in Transformers with Mutual Information—0
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks—0
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models—0
EnKhCorp1.0: An English–Khasi Corpus—0
Enlarging Scarce In-domain English-Croatian Corpus for SMT of MOOCs Using Serbian—0
Enlisting the Ghost: Modeling Empty Categories for Machine Translation—0
Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation—0
Cost Weighting for Neural Machine Translation Domain Adaptation—0
Enriching Non-Autoregressive Transformer with Syntactic and SemanticStructures for Neural Machine Translation—0
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation—0
Enriching Parallel Corpora for Statistical Machine Translation with Semantic Negation Rephrasing—0
Enriching Phrase Tables for Statistical Machine Translation Using Mixed Embeddings—0
Enriching Source for English-to-Urdu Machine Translation—0
Enriching the Transformer with Linguistic Factors for Low-Resource Machine Translation—0
Building a robust sentiment lexicon with (almost) no resource—0
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017—0
Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation—0
Ensemble Distillation for Neural Machine Translation—0
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three—0
Ensemble Fine-tuned mBERT for Translation Quality Estimation—0
Ensemble Learning for Multi-Source Neural Machine Translation—0
Ensemble of Translators with Automatic Selection of the Best Translation -- the submission of FOKUS to the WMT 18 biomedical translation task --—0
Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report—0
Ensemble Triangulation for Statistical Machine Translation—0
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Ensembling Various Dependency Parsers: Adopting Turbo Parser for Indian Languages—0
Attention-based Multimodal Neural Machine Translation—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