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 9351–9400 of 10752 papers

TitleStatusHype
Coreference Strategies in English-German Translation—0
Co-Regression for Cross-Language Review Rating Prediction—0
Co-regularizing character-based and word-based models for semi-supervised Chinese word segmentation—0
Corpora for Cross-Language Information Retrieval in Six Less-Resourced Languages—0
Corpora for Document-Level Neural Machine Translation—0
Corpora Generation for Grammatical Error Correction—0
Corpus and dictionary development for classifiers/quantifiers towards a French-Japanese machine translation—0
Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation—0
Corpus Creation and Evaluation for Speech-to-Text and Speech Translation—0
Corpus Creation and Initial SMT Experiments between Spanish and Shipibo-konibo—0
Corpus development for machine translation between standard and dialectal varieties—0
Corpus-Driven Thematic Hierarchy Induction—0
Corpus Linguistics, Translation and Error Analysis—0
Correct-and-Memorize: Learning to Translate from Interactive Revisions—0
Correcting FLORES Evaluation Dataset for Four African Languages—0
Correcting Length Bias in Neural Machine Translation—0
Correcting Preposition Errors in Learner English Using Error Case Frames and Feedback Messages—0
Correcting Serial Grammatical Errors based on N-grams and Syntax—0
Correction of Errors in Preference Ratings from Automated Metrics for Text Generation—0
Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods—0
Correlating decoding events with errors in Statistical Machine Translation—0
Correlation between Similarity Measures for Inter-Language Linked Wikipedia Articles—0
Cortex Neural Network: learning with Neural Network groups—0
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving—0
Cost-benefit Analysis of Two-Stage Conditional Random Fields based English-to-Chinese Machine Transliteration—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost Optimization in Crowdsourcing Translation: Low cost translations made even cheaper—0
Cost-Sensitive Training for Autoregressive Models—0
Cost Weighting for Neural Machine Translation Domain Adaptation—0
Counterfactual Learning for Machine Translation: Degeneracies and Solutions—0
Counterfactual Learning from Bandit Feedback under Deterministic Logging: A Case Study in Statistical Machine Translation—0
Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation—0
Counting in Language with RNNs—0
Counting What Counts: Decompounding for Keyphrase Extraction—0
Coverage and Cynicism: The AFRL Submission to the WMT 2018 Parallel Corpus Filtering Task—0
Coverage Embedding Models for Neural Machine Translation—0
CPJD Corpus: Crowdsourced Parallel Speech Corpus of Japanese Dialects—0
CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge—0
Crafting Adversarial Examples for Neural Machine Translation—0
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation—0
CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models—0
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT—0
Creating a massively parallel Bible corpus—0
Creating and using large monolingual parallel corpora for sentential paraphrase generation—0
Creating Arabic LLM Prompts at Scale—0
Creating Domain-Specific Translation Memories for Machine Translation Fine-tuning: The TRENCARD Bilingual Cardiology Corpus—0
Creating Interactive Macaronic Interfaces for Language Learning—0
Creating rich online dictionaries for the Lao--French language pair, reusable for Machine Translation—0
Creation of comparable corpora for English-Urdu, Arabic, Persian—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
5AdminBLEU score43.8—Unverified
6Transformer (ADMIN init)BLEU 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