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 10051–10100 of 10752 papers

TitleStatusHype
Word Root Finder: a Morphological Segmentor Based on CRF—0
UMCC\_DLSI: Multidimensional Lexical-Semantic Textual Similarity—0
UMCC\_DLSI\_SemSim: Multilingual System for Measuring Semantic Textual Similarity—0
UMCC\_DLSI: Textual Similarity based on Lexical-Semantic features—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation—0
UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity—0
UMMU@QALB-2015 Shared Task: Character and Word level SMT pipeline for Automatic Error Correction of Arabic Text—0
Word Segmentation of Informal Arabic with Domain Adaptation—0
UNAL: Discriminating between Literal and Figurative Phrasal Usage Using Distributional Statistics and POS tags—0
Unbabel's Participation in the WMT16 Word-Level Translation Quality Estimation Shared Task—0
Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task—0
Unbabel's Submission to the WMT2019 APE Shared Task: BERT-based Encoder-Decoder for Automatic Post-Editing—0
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training—0
Uncertainty-Aware Curriculum Learning for Neural Machine Translation—0
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT—0
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation—0
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models—0
Uncertainty Estimation in Autoregressive Structured Prediction—0
UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models—0
Uncovering Distributional Differences between Synonyms and Antonyms in a Word Space Model—0
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective—0
Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray—0
The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation—0
Word Sense-Aware Machine Translation: Including Senses as Contextual Features for Improved Translation Models—0
Understanding and Enhancing the Use of Context for Machine Translation—0
Understanding and Explicitly Measuring Linguistic and Stylistic Properties of Deception via Generation and Translation—0
The Language Library: supporting community effort for collective resource production—0
Understanding and Improving Hidden Representations for Neural Machine Translation—0
Word Sense Disambiguation: A comprehensive knowledge exploitation framework—0
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder—0
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation—0
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation—0
Understanding and Mitigating the Uncertainty in Zero-Shot Translation—0
The Four Stages of Machine Translation Acceptance in a Freelancer's Life—0
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization—0
Understanding Few-Shot Multi-Task Representation Learning Theory—0
The Language Demographics of Amazon Mechanical Turk—0
Understanding Knowledge Distillation in Non-autoregressive Machine Translation—0
Understanding Learning Dynamics for Neural Machine Translation—0
Word Sense Disambiguation Improves Information Retrieval—0
Understanding Multi-Head Attention in Abstractive Summarization—0
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models—0
Understanding Parameter Sharing in Transformers—0
Understanding Pre-Editing for Black-Box Neural Machine Translation—0
Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English—0
The Language Application Grid—0
Understanding the Effect of Textual Adversaries in Multimodal Machine Translation—0
Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation—0
Understanding the effects of word-level linguistic annotations in under-resourced 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