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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 2401–2450 of 10752 papers

TitleStatusHype
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder—0
Evaluating MT Systems: A Theoretical Framework—0
Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMETCode0
Survey of Hallucination in Natural Language Generation—0
Can We Generate Shellcodes via Natural Language? An Empirical StudyCode1
Machine Translation from Signed to Spoken Languages: State of the Art and Challenges—0
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer ModelsCode1
Pirá: A Bilingual Portuguese-English Dataset for Question-Answering about the OceanCode0
The Ecological Footprint of Neural Machine Translation SystemsCode0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
A Survey on Retrieval-Augmented Text Generation—0
Examining Scaling and Transfer of Language Model Architectures for Machine Translation—0
Anticipation-Free Training for Simultaneous Machine TranslationCode0
Prabhupadavani: A Code-mixed Speech Translation Data for 25 LanguagesCode1
Learning How to Translate North Korean through South Korean—0
Impact of representation matching with neural machine translationCode0
Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniquesCode0
DiscoScore: Evaluating Text Generation with BERT and Discourse CoherenceCode1
CsFEVER and CTKFacts: Acquiring Czech data for fact verificationCode0
BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic—0
Supervised Visual Attention for Simultaneous Multimodal Machine Translation—0
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine TranslationCode1
VISA: An Ambiguous Subtitles Dataset for Visual Scene-Aware Machine TranslationCode1
Improving Neural Machine Translation by Denoising Training—0
Extending the Vocabulary of Fictional Languages using Neural Networks—0
Computational Methods for Single-Cell Multi-Omics Integration and Alignment—0
Syntax-based data augmentation for Hungarian-English machine translationCode0
Discovering Non-Monotonic Autoregressive Ordering for Text Generation Models using Sinkhorn Distributions—0
Understanding Few-Shot Multi-Task Representation Learning Theory—0
Improving Robustness in Multilingual Machine Translation via Data Augmentation—0
Document-level Neural Machine Translation Using Dependency RST Structure—0
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation—0
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages—0
Quantifying Synthesis and Fusion and their Impact on Machine Translation—0
Non-Autoregressive Machine Translation: It's Not as Fast as it Seems—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation—0
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation—0
The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer—0
Multi-way VNMT for UGC: Improving Robustness and Capacity via Mixture Density Networks—0
End-to-end Speech Translation with Spoken-to-Written Style Conversion—0
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance—0
Latent Group Dropout for Multilingual and Multidomain Machine Translation—0
Efficient Machine Translation Domain Adaptation—0
Can Multilinguality Benefit Non-autoregressive Machine Translation?—0
DEEP: DEnoising Entity Pre-training for Neural Machine Translation—0
Improving both domain robustness and domain adaptability 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