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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 5101–5150 of 10752 papers

TitleStatusHype
May I take your order? A Neural Model for Extracting Structured Information from Conversations—0
MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model—0
MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods—0
MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set—0
MDQE: A More Accurate Direct Pretraining for Machine Translation Quality Estimation—0
Meaningless yet meaningful: Morphology grounded subword-level NMT—0
Meaning Representation of Numeric Fused-Heads in UCCA—0
Meaning Unit Segmentation in English and Chinese: a New Approach to Discourse Phenomena—0
Mean Machine Translations: On Gender Bias in Icelandic Machine Translations—0
MEANT 2.0: Accurate semantic MT evaluation for any output language—0
MEANT at WMT 2013: A Tunable, Accurate yet Inexpensive Semantic Frame Based MT Evaluation Metric—0
Measurement of Progress in Machine Translation—0
Measuring and Improving Faithfulness of Attention in Neural Machine Translation—0
Measuring and Mitigating Name Biases in Neural Machine Translation—0
Measuring Cognitive Translation Effort with Activity Units—0
Measuring Comparability of Documents in Non-Parallel Corpora for Efficient Extraction of (Semi-)Parallel Translation Equivalents—0
Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation—0
Measuring Immediate Adaptation Performance for Neural Machine Translation—0
Measuring Interlanguage: Native Language Identification with L1-influence Metrics—0
Measuring Machine Translation Errors in New Domains—0
Measuring Memorization Effect in Word-Level Neural Networks Probing—0
Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model—0
Measuring `Registerness' in Human and Machine Translation: A Text Classification Approach—0
Measuring Robustness for NLP—0
Measuring sentence parallelism using Mahalanobis distances: The NRC unsupervised submissions to the WMT18 Parallel Corpus Filtering shared task—0
Measuring Sentiment Bias in Machine Translation—0
Measuring the Adequacy of Cross-Lingual Paraphrases in a Machine Translation Setting—0
Measuring the behavioral impact of machine translation quality improvements with A/B testing—0
Measuring the Divergence of Dependency Structures Cross-Linguistically to Improve Syntactic Projection Algorithms—0
Measuring the Effect of Conversational Aspects on Machine Translation Quality—0
Measuring the Effects of Human and Machine Translation on Website Engagement—0
Measuring the Impact of Spelling Errors on the Quality of Machine Translation—0
Measuring the Influence of Long Range Dependencies with Neural Network Language Models—0
Measuring the Limit of Semantic Divergence for English Tweets.—0
Measuring Translationese across Levels of Expertise: Are Professionals more Surprising than Students?—0
Measuring Uncertainty in Translation Quality Evaluation (TQE)—0
MEDLINE as a Parallel Corpus: a Survey to Gain Insight on French-, Spanish- and Portuguese-speaking Authors' Abstract Writing Practice—0
MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection—0
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation—0
MeetDot: Videoconferencing with Live Translation Captions—0
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation—0
Memorization or Reasoning? Exploring the Idiom Understanding of LLMs—0
Memory-augmented Chinese-Uyghur Neural Machine Translation—0
Memory-augmented Neural Machine Translation—0
Memory Efficient Adaptive Optimization—0
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model—0
Memory-enhanced Decoder for Neural Machine Translation—0
Memory Representation in Transformer—0
Mention Attention for Pronoun Translation—0
Merged bilingual trees based on Universal Dependencies in 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