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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 6001–6050 of 10752 papers

TitleStatusHype
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages—0
Optimising the Machine Translation Post-editing Workflow—0
Optimization for Efficient Determination of Chunk in Automatic Evaluation for Machine Translation—0
Optimization for Statistical Machine Translation: A Survey—0
Optimization Strategies for Online Large-Margin Learning in Machine Translation—0
Optimized Online Rank Learning for Machine Translation—0
Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection—0
Optimizing Deep Transformers for Chinese-Thai Low-Resource Translation—0
Optimizing example selection for retrieval-augmented machine translation with translation memories—0
Optimizing for Sentence-Level BLEU+1 Yields Short Translations—0
Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability—0
Optimizing Non-Autoregressive Transformers with Contrastive Learning—0
Optimizing Segmentation Granularity for Neural Machine Translation—0
Optimizing Segmentation Strategies for Simultaneous Speech Translation—0
Optimizing transformer-based machine translation model for single GPU training: a hyperparameter ablation study—0
Optimizing Transformer for Low-Resource Neural Machine Translation—0
Optimizing Word Alignments with Better Subword Tokenization—0
OPT: Oslo--Potsdam--Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing—0
OPUS-CAT: Desktop NMT with CAT integration and local fine-tuning—0
OPUS-MT – Building open translation services for the World—0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
Scrambled Translation Problem: A Problem of Denoising UNMT—0
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning—0
Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance—0
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation—0
Original-Transcribed Text Alignment for Manyosyu Written by Old Japanese Language—0
Orthogonality of Syntax and Semantics within Distributional Spaces—0
Orthographic and Morphological Processing for Persian-to-English Statistical Machine Translation—0
Orthographic Features for Bilingual Lexicon Induction—0
OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media—0
OSU Multimodal Machine Translation System Report—0
Our Neural Machine Translation Systems for WAT 2019—0
Out-of-Order Decoding for Robust Neural Machine Translation—0
Out-of-the-box Universal Romanization Tool uroman—0
Overcoming a Theoretical Limitation of Self-Attention—0
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation—0
Overcoming Resistance: The Normalization of an Amazonian Tribal Language—0
Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation—0
Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation—0
Overcoming Vocabulary Constraints with Pixel-level Fallback—0
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation—0
Overview of the 1st Workshop on Asian Translation—0
Overview of the 2013 ALTA Shared Task—0
Overview of the 2nd Workshop on Asian Translation—0
Overview of the 3rd Workshop on Asian Translation—0
Overview of the 4th Workshop on Asian Translation—0
Overview of the IWSLT 2017 Evaluation Campaign—0
Overview of the Second BUCC Shared Task: Spotting Parallel Sentences in Comparable Corpora—0
Overview of the Shared Task on Machine Translation in Dravidian Languages—0
PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification—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