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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 1951–2000 of 10752 papers

TitleStatusHype
End-to-end Speech-to-Punctuated-Text Recognition—0
Vision-and-Language PretrainingCode0
TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and TextsCode1
Can Language Models Make Fun? A Case Study in Chinese Comical CrosstalkCode1
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer—0
Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence TransformersCode0
Latent Group Dropout for Multilingual and Multidomain Machine Translation—0
Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition—0
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity—0
How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train & Few-shot Cross-lingual Transfer—0
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance—0
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation—0
Language Model Augmented Monotonic Attention for Simultaneous Translation—0
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations—0
SURF: Semantic-level Unsupervised Reward Function for Machine Translation—0
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine TranslationCode0
Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity—0
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information—0
HuaAMS at SemEval-2022 Task 8: Combining Translation and Domain Pre-training for Cross-lingual News Article Similarity—0
Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource LanguageCode0
Evaluating Gender Bias Transfer from Film Data—0
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text GenerationCode0
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems—0
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework—0
Analysing the Correlation between Lexical Ambiguity and Translation Quality in a Multimodal Setting using WordNet—0
A Taxonomy of Bias-Causing Ambiguities in Machine Translation—0
Uppsala University at SemEval-2022 Task 1: Can Foreign Entries Enhance an English Reverse Dictionary?—0
Neural Networks in a Product of Hyperbolic Spaces—0
Opportunities for Human-centered Evaluation of Machine Translation Systems—0
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.—0
Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator—0
Code Translation with Compiler Representations—0
Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations—0
Shifts 2.0: Extending The Dataset of Real Distributional ShiftsCode2
GERNERMED++: Transfer Learning in German Medical NLPCode0
Revisiting Label Smoothing and Knowledge Distillation Compatibility: What was Missing?Code1
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
Probing Causes of Hallucinations in Neural Machine Translations—0
Evaluation of Semantic Answer Similarity MetricsCode0
Answer Fast: Accelerating BERT on the Tensor Streaming Processor—0
Scaling Autoregressive Models for Content-Rich Text-to-Image GenerationCode0
Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpus—0
Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic RolesCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
The ITU Faroese Pairs Dataset—0
Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs—0
An Extractive-and-Abstractive Framework for Source Code SummarizationCode0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
An Exploration of Post-Editing Effectiveness in Text Summarization—0
Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization—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