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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 6451–6500 of 10752 papers

TitleStatusHype
Findings of the WMT 2018 Shared Task on Automatic Post-Editing—0
Findings of the WMT 2018 Shared Task on Quality Estimation—0
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering—0
Findings of the WMT 2019 Shared Task on Automatic Post-Editing—0
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions—0
Findings of the WMT 2019 Shared Tasks on Quality Estimation—0
Findings of the WMT 2020 Shared Task on Machine Translation Robustness—0
Findings of the WMT 2020 Shared Task on Automatic Post-Editing—0
Findings of the WMT 2020 Shared Task on Quality Estimation—0
Findings of the WMT 2020 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT—0
Findings of the WMT 2021 Shared Task on Efficient Translation—0
Findings of the WMT 2021 Shared Task on Quality Estimation—0
Findings of the WMT 2021 Shared Task on Large-Scale Multilingual Machine Translation—0
Findings of the WMT 2022 Shared Task on Translation Suggestion—0
Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs—0
Findings of the WMT Shared Task on Machine Translation Using Terminologies—0
Finding Structure in Figurative Language: Metaphor Detection with Topic-based Frames—0
Finding the Answers with Definition Models—0
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation—0
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation—0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Fine-Grained Arabic Dialect Identification—0
Fine-Grained Attention Mechanism for Neural Machine Translation—0
Fine-Grained Discourse Structures in Continuation Semantics—0
Fine-Grained Error Analysis on English-to-Japanese Machine Translation in the Medical Domain—0
Fine-grained evaluation of German-English Machine Translation based on a Test Suite—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite—0
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically-motivated Test Suite—0
Fine-Grained Focus for Pinpointing Positive Implicit Meaning from Negated Statements—0
Fine-grained Gender Control in Machine Translation with Large Language Models—0
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text—0
Fine-grained linguistic evaluation for state-of-the-art Machine Translation—0
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings—0
Fine-grained Semantic Textual Similarity for Serbian—0
Finetuning a Kalaallisut-English machine translation system using web-crawled data—0
Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation—0
Fine-Tuning for Neural Machine Translation with Limited Degradation across In- and Out-of-Domain Data—0
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice?—0
Fine-tuning Neural Machine Translation on Gender-Balanced Datasets—0
Fine-tuning on Clean Data for End-to-End Speech Translation: FBK @ IWSLT 2018—0
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages—0
Fineweb-Edu-Ar: Machine-translated Corpus to Support Arabic Small Language Models—0
Finite-State Morphological Analysis for Marathi—0
Finite-state morphological transducers for three Kypchak languages—0
Finnish SQuAD: A Simple Approach to Machine Translation of Span Annotations—0
First approach toward Semantic Role Labeling for Basque—0
First Attempt at Building Parallel Corpora for Machine Translation of Northeast India's Very Low-Resource Languages—0
First Experiments with Neural Translation of Informal to Formal Mathematics—0
First Result on Arabic Neural 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