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 6201–6250 of 10752 papers

TitleStatusHype
PLUTO: Automated Solutions for Patent Translation—0
PMKI: an European Commission action for the interoperability, maintainability and sustainability of Language Resources—0
PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation—0
PNAT: Non-autoregressive Transformer by Position Learning—0
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation—0
Pointing the Unknown Words—0
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions—0
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2014—0
Polish - English Speech Statistical Machine Translation Systems for the IWSLT 2013—0
Polish -English Statistical Machine Translation of Medical Texts—0
Polish to English Statistical Machine Translation—0
Political claim identification and categorization in a multilingual setting: First experiments—0
POLYGLOT-NER: Massive Multilingual Named Entity Recognition—0
Polylingual Tree-Based Topic Models for Translation Domain Adaptation—0
Polylingual Wordnet—0
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation—0
Poor man's lemmatisation for automatic error classification—0
Portable, layer-wise task performance monitoring for NLP models—0
PORT: a Precision-Order-Recall MT Evaluation Metric for Tuning—0
PosEdiOn: Post-Editing Assessment in PythOn—0
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network—0
Positive Diversity Tuning for Machine Translation System Combination—0
POS-Tagging based Neural Machine Translation System for European Languages using Transformers—0
Pos-tagging different varieties of Occitan with single-dialect resources—0
POS Tagging for Arabic Tweets—0
POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task—0
Postech's System Description for Medical Text Translation Task—0
Post-editing a chapter of a specialized textbook into 7 languages: importance of terminological proximity with English for productivity—0
Post-Editing Evaluations: Trade-offs between Novice and Professional Participants—0
Post-Editing Job Profiles for Subtitlers—0
Post-editing Productivity with Neural Machine Translation: An Empirical Assessment of Speed and Quality in the Banking and Finance Domain—0
Post-\'edition statistique pour l'adaptation aux domaines de sp\'ecialit\'e en traduction automatique (Statistical Post-Editing of Machine Translation for Domain Adaptation) [in French]—0
Post-edits Are Preferences Too—0
Post-ordering by Parsing for Japanese-English Statistical Machine Translation—0
Potential and limits of distributional approaches for semantic relatedness—0
Potential and Limits of Using Post-edits as Reference Translations for MT Evaluation—0
Power-Law Distributions for Paraphrases Extracted from Bilingual Corpora—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Practical Attacks on Machine Translation using Paraphrase—0
Practical Neural Machine Translation—0
Practical Perspectives on Quality Estimation for Machine Translation—0
Pragmatic information in translation: a corpus-based study of tense and mood in English and German—0
Pragmatic Neural Language Modelling in Machine Translation—0
Pre-Computable Multi-Layer Neural Network Language Models—0
Predicate-Argument Structure-based Preordering for Japanese-English Statistical Machine Translation of Scientific Papers—0
Predictability of Distributional Semantics in Derivational Word Formation—0
Predicting Anchored Text from Translation Memories for Machine Translation Using Deep Learning Methods—0
Predicting and Using Implicit Discourse Elements in Chinese-English Translation—0
Predicting and Using Target Length in Neural Machine Translation—0
Predicting Attention Sparsity in Transformers—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