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 3201–3250 of 10752 papers

TitleStatusHype
EED: Extended Edit Distance Measure for Machine Translation—0
Analysing Coreference in Transformer Outputs—0
English-Portuguese Biomedical Translation Task Using a Genuine Phrase-Based Statistical Machine Translation Approach—0
Effective Bitext Extraction From Comparable Corpora Using a Combination of Three Different Approaches—0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024—0
Crafting Adversarial Examples for Neural Machine Translation—0
Effective Domain Mixing for Neural Machine Translation—0
Effective General-Domain Data Inclusion for the Machine Translation Task by Vanilla Transformers—0
Effective Incorporation of Source Syntax into Hierarchical Phrase-based Translation—0
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions—0
Effectively pretraining a speech translation decoder with Machine Translation data—0
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge—0
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
Effective Quantization Approaches for Recurrent Neural Networks—0
Effective Selection of Translation Model Training Data—0
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs—0
CPJD Corpus: Crowdsourced Parallel Speech Corpus of Japanese Dialects—0
Effective Use of Target-side Context for Neural Machine Translation—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Effects of Prompt Length on Domain-specific Tasks for Large Language Models—0
Efficient and High-Quality Neural Machine Translation with OpenNMT—0
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
An Aligned French-Chinese corpus of 10K segments from university educational material—0
Efficient Bilingual Generalization from Neural Transduction Grammar Induction—0
Enhancing AI-based Generation of Software Exploits with Contextual Information—0
Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora—0
English Resource Semantics—0
Block-Sparse Recurrent Neural Networks—0
English to Bengali Multimodal Neural Machine Translation using Transliteration-based Phrase Pairs Augmentation—0
Efficient Implementation of Beam-Search Incremental Parsers—0
English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs—0
Efficient Inference For Neural Machine Translation—0
Efficient Inner-to-outer Greedy Algorithm for Higher-order Labeled Dependency Parsing—0
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation—0
Enhanced Auto Language Prediction with Dictionary Capsule -- A Novel Approach—0
Coverage Embedding Models for Neural Machine Translation—0
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages—0
Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation—0
Book Review: Bayesian Analysis in Natural Language Processing by Shay Cohen—0
Efficient Machine Translation Domain Adaptation—0
Book Review: Bitext Alignment by J\"org Tiedemann—0
A global AI community requires language-diverse publishing—0
Efficient Machine Translation with Model Pruning and Quantization—0
Attention Calibration for Transformer in Neural Machine Translation—0
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages—0
Efficient Neural Machine Translation with Prior Word Alignment—0
Coverage and Cynicism: The AFRL Submission to the WMT 2018 Parallel Corpus Filtering Task—0
Attention-based Vocabulary Selection for NMT Decoding—0
Efficient Sample-Specific Encoder Perturbations—0
0/1 Deep Neural Networks via Block Coordinate Descent—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