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 10301–10350 of 10752 papers

TitleStatusHype
Using crowdsourcing system for creating site-specific statistical machine translation engine—0
Using Deep Object Features for Image Descriptions—0
Using Discourse Information for Paraphrase Extraction—0
Using Discourse Structure Improves Machine Translation Evaluation—0
Using Distributional Similarity of Multi-way Translations to Predict Multiword Expression Compositionality—0
Using Document Similarity Methods to create Parallel Datasets for Code Translation—0
Using Domain-specific and Collaborative Resources for Term Translation—0
Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora—0
Using English Baits to Catch Serbian Multi-Word Terminology—0
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification—0
Using Factored Word Representation in Neural Network Language Models—0
Using Feature Structures to Improve Verb Translation in English-to-German Statistical MT—0
Using Finite State Transducers for Helping Foreign Language Learning—0
Using Gaze to Predict Text Readability—0
Using Grammatical and Semantic Correction Model to Improve Chinese-to-Taiwanese Machine Translation Fluency—0
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition—0
Using Images to Improve Machine-Translating E-Commerce Product Listings.—0
Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT—0
“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)—0
Using Linguistic Data for English and Spanish Verb-Noun Combination Identification—0
XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference—0
Using Linked Disambiguated Distributional Networks for Word Sense Disambiguation—0
EAT: a simple and versatile semantic representation format for multi-purpose NLP—0
Using LSTM to Translate French to Senegalese Local Languages: Wolof as a Case Study—0
Using Machine Learning to Detect Fraudulent SMSs in Chichewa—0
Using Machine Translation to Augment Multilingual Classification—0
Using Machine Translation to Localize Task Oriented NLG Output—0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning—0
Using Mechanical Turk to Build Machine Translation Evaluation Sets—0
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment—0
Using Morphemes from Agglutinative Languages like Quechua and Finnish to Aid in Low-Resource Translation—0
Using Multilingual Topic Models for Improved Alignment in English-Hindi MT—0
Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality—0
Using natural language prompts for machine translation—0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
Using Neural Networks for Modeling and Representing Natural Languages—0
Using Noun Similarity to Adapt an Acceptability Measure for Persian Light Verb Constructions—0
Using on-line available sources of bilingual information for word-level machine translation quality estimation—0
Using Parallel Features in Parsing of Machine-Translated Sentences for Correction of Grammatical Errors—0
Using Parallel Texts and Lexicons for Verbal Word Sense Disambiguation—0
Using paraphrases for improving first story detection in news and Twitter—0
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation—0
Using Recurrent Neural Network for Learning Expressive Ontologies—0
Using Related Languages to Enhance Statistical Language Models—0
Enhanced back-translation for low resource neural machine translation using self-training—0
Using Semantic Role Labeling to Improve Neural Machine Translation—0
Using semi-experts to derive judgments on word sense alignment: a pilot study—0
Using Sense-labeled Discourse Connectives for Statistical Machine Translation—0
Using Senses in HMM Word Alignment—0
Using SMT for OCR Error Correction of Historical Texts—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