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 10701–10750 of 10752 papers

TitleStatusHype
TMU Japanese-English Multimodal Machine Translation System for WAT 2020—0
TMU Japanese-English Neural Machine Translation System using Generative Adversarial Network for WAT 2018—0
TMU NMT System with Automatic Post-Editing by Multi-Source Levenshtein Transformer for the Restricted Translation Task of WAT 2022—0
TMU NMT System with Japanese BART for the Patent task of WAT 2021—0
Tmuse: Lexical Network Exploration—0
TNT-NLG, System 1: Using a statistical NLG to massively augment crowd-sourced data for neural generation—0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes—0
To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation—0
To Case or not to case: Evaluating Casing Methods for Neural Machine Translation—0
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation—0
Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging—0
Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production—0
The OpenNMT Neural Machine Translation Toolkit: 2020 Edition—0
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia—0
The GermaParl Corpus of Parliamentary Protocols—0
Token-Level Metaphor Detection using Neural Networks—0
Token-wise Curriculum Learning for Neural Machine Translation—0
Tokyo Metropolitan University Neural Machine Translation System for WAT 2017—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
Tolerant BLEU: a Submission to the WMT14 Metrics Task—0
The Nunavut Hansard Inuktitut--English Parallel Corpus 3.0 with Preliminary Machine Translation Results—0
Tools and Guidelines for Principled Machine Translation Development—0
Tools for plWordNet Development. Presentation and Perspectives—0
To Optimize, or Not to Optimize, That Is the Question: TelU-KU Models for WMT21 Large-Scale Multilingual Machine Translation—0
Top a Splitter: Using Distributional Semantics for Improving Compound Splitting—0
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing—0
Topic Adaptation for the Automatic Translation of News Articles (Adaptation th\'ematique pour la traduction automatique de d\'ep\^eches de presse) [in French]—0
Topic-Centric Unsupervised Multi-Document Summarization of Scientific and News Articles—0
Topic-Informed Neural Machine Translation—0
Topic Modeling-based Domain Adaptation for System Combination—0
Topic Models: Accounting Component Structure of Bigrams—0
Topic Models for Dynamic Translation Model Adaptation—0
Topic Models + Word Alignment = A Flexible Framework for Extracting Bilingual Dictionary from Comparable Corpus—0
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models—0
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation—0
TorchScale: Transformers at Scale—0
Toshiba MT System Description for the WAT2014 Workshop—0
Toshiba MT System Description for the WAT2015 Workshop—0
The NRC System for Discriminating Similar Languages—0
To Smooth or not to Smooth? On Compatibility between Label Smoothing and Knowledge Distillation—0
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation—0
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages—0
Zero Object Resolution in Korean—0
Touch-Based Pre-Post-Editing of Machine Translation Output—0
To Understand Representation of Layer-aware Sequence Encoders as Multi-order-graph—0
Toward a Comparable Corpus of Latvian, Russian and English Tweets—0
Toward a full-scale neural machine translation in production: the Booking.com use case—0
Toward Better Chinese Word Segmentation for SMT via Bilingual Constraints—0
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings—0
Toward Determining the Comprehensibility of Machine Translations—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