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 6901–6950 of 10752 papers

TitleStatusHype
SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity—0
SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation—0
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues—0
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes—0
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation—0
Semi-automatic Filtering of Translation Errors in Triangle Corpus—0
Semi-Automatic Sign Language Corpora Annotation using Lexical Representations of Signs—0
Semi-Autoregressive Training Improves Mask-Predict Decoding—0
Semi-Markov Phrase-Based Monolingual Alignment—0
Semi-Supervised and Unsupervised Sense Annotation via Translations—0
Semi-supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In-domain Data—0
Semi-Supervised Disfluency Detection—0
Semi-Supervised Learning for Neural Machine Translation—0
Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification—0
Semi-Supervised Neural Machine Translation with Language Models—0
Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages—0
Semi-Supervised Representation Learning for Cross-Lingual Text Classification—0
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Semi-supervised Training Data Generation for Multilingual Question Answering—0
SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages—0
SENCORPUS: A French-Wolof Parallel Corpus—0
Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering—0
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity—0
SenseSpotting: Never let your parallel data tie you to an old domain—0
Sensible: L2 Translation Assistance by Emulating the Manual Post-Editing Process—0
Sentence Alignment Methods for Improving Text Simplification Systems—0
Sentence Alignment using Unfolding Recursive Autoencoders—0
Sentence Boundary Augmentation For Neural Machine Translation Robustness—0
Sentence Boundary Detection for Social Media Text—0
Sentence Compression with Semantic Role Constraints—0
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation—0
Sentence Embedding for Neural Machine Translation Domain Adaptation—0
Sentence Encoding with Tree-constrained Relation Networks—0
Sentence-Level Adaptation for Low-Resource Neural Machine Translation—0
Sentence-level Aggregation of Lexical Metrics Correlates Stronger with Human Judgements than Corpus-level Aggregation—0
Sentence-Level Agreement for Neural Machine Translation—0
Sentence Level Dialect Identification for Machine Translation System Selection—0
Sentence Level Dialect Identification in Arabic—0
Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing—0
Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation—0
Sentence-Level Quality Estimation for MT System Combination—0
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing—0
Sentence Rephrasing for Parsing Sentences with OOV Words—0
Sentence Simplification by Monolingual Machine Translation—0
Sentence Simplification with Memory-Augmented Neural Networks—0
Sentence Weighting for Neural Machine Translation Domain Adaptation—0
Sentence-wise Smooth Regularization for Sequence to Sequence Learning—0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel—0
Sentential Paraphrasing as Black-Box Machine Translation—0
Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems—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