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

Showing 88018850 of 10752 papers

TitleStatusHype
A case study on supervised classification of Swedish pseudo-coordination0
Topic Models: Accounting Component Structure of Bigrams0
Automatic conversion of colloquial Finnishto standard Finnish0
Invited Talk: How Much Information Does a Human Translator Add to the Original?0
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation ExtractionCode0
The Logic of AMR: Practical, Unified, Graph-Based Sentence Semantics for NLP0
Learning to Interpret and Describe Abstract Scenes0
A Comparison of Update Strategies for Large-Scale Maximum Expected BLEU Training0
Deep Learning and Continuous Representations for Natural Language Processing0
Crowdsourcing for NLP0
Social Media Predictive Analytics0
Response-based Learning for Machine Translation of Open-domain Database Queries0
Aligning Sentences from Standard Wikipedia to Simple Wikipedia0
When and why are log-linear models self-normalizing?0
Fast and Accurate Preordering for SMT using Neural Networks0
APRO: All-Pairs Ranking Optimization for MT Tuning0
Accurate Evaluation of Segment-level Machine Translation Metrics0
Two/Too Simple Adaptations of Word2Vec for Syntax ProblemsCode0
Multi-Task Word Alignment Triangulation for Low-Resource Languages0
Multi-Target Machine Translation with Multi-Synchronous Context-free Grammars0
Empty Category Detection With Joint Context-Label Embeddings0
Morphological Modeling for Machine Translation of English-Iraqi Arabic Spoken Dialogs0
An Incremental Algorithm for Transition-based CCG Parsing0
LR Parsing for LCFRS0
Leveraging Small Multilingual Corpora for SMT Using Many Pivot Languages0
Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval0
Data-driven sentence generation with non-isomorphic trees0
Inflection Generation as Discriminative String Transduction0
Cost Optimization in Crowdsourcing Translation: Low cost translations made even cheaper0
A Transition-based Algorithm for AMR ParsingCode0
Continuous Adaptation to User Feedback for Statistical Machine Translation0
Context-Dependent Automatic Response Generation Using Statistical Machine Translation Techniques0
Spinning Straw into Gold: Using Free Text to Train Monolingual Alignment Models for Non-factoid Question Answering0
The Geometry of Statistical Machine Translation0
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation0
Bengali to Assamese Statistical Machine Translation using Moses (Corpus Based)0
From a Distance: Using Cross-lingual Word Alignments for Noun Compound Bracketing0
Alignment of Eye Movements and Spoken Language for Semantic Image Understanding0
Layers of Interpretation: On Grammar and Compositionality0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings0
Crowdsourced Word Sense Annotations and Difficult Words and Examples0
Hierarchical Statistical Semantic Realization for Minimal Recursion Semantics0
Towards Using Machine Translation Techniques to Induce Multilingual Lexica of Discourse Markers0
Yara Parser: A Fast and Accurate Dependency ParserCode0
Phrase database Approach to structural and semantic disambiguation in English-Korean Machine Translation0
genCNN: A Convolutional Architecture for Word Sequence Prediction0
Long Short-Term Memory Over Tree Structures0
On Using Monolingual Corpora in Neural Machine Translation0
Syntax-based Deep Matching of Short Texts0
Context-Dependent Translation Selection Using Convolutional Neural Network0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Transformer Cycle (Rev)BLEU score35.14Unverified
2Noisy back-translationBLEU score35Unverified
3Transformer+Rep(Uni)BLEU score33.89Unverified
4T5-11BBLEU score32.1Unverified
5BiBERTBLEU score31.26Unverified
6Transformer + R-DropBLEU score30.91Unverified
7Bi-SimCutBLEU score30.78Unverified
8BERT-fused NMTBLEU score30.75Unverified
9Data Diversification - TransformerBLEU score30.7Unverified
10SimCutBLEU score30.56Unverified
#ModelMetricClaimedVerifiedStatus
1Transformer+BT (ADMIN init)BLEU score46.4Unverified
2Noisy back-translationBLEU score45.6Unverified
3mRASP+Fine-TuneBLEU score44.3Unverified
4Transformer + R-DropBLEU score43.95Unverified
5Transformer (ADMIN init)BLEU score43.8Unverified
6AdminBLEU score43.8Unverified
7BERT-fused NMTBLEU score43.78Unverified
8MUSE(Paralllel Multi-scale Attention)BLEU score43.5Unverified
9T5BLEU score43.4Unverified
10Local Joint Self-attentionBLEU score43.3Unverified
#ModelMetricClaimedVerifiedStatus
1PiNMTBLEU score40.43Unverified
2BiBERTBLEU score38.61Unverified
3Bi-SimCutBLEU score38.37Unverified
4Cutoff + Relaxed Attention + LMBLEU score37.96Unverified
5DRDABLEU score37.95Unverified
6Transformer + R-Drop + CutoffBLEU score37.9Unverified
7SimCutBLEU score37.81Unverified
8Cutoff+KneeBLEU score37.78Unverified
9CutoffBLEU score37.6Unverified
10CipherDAugBLEU score37.53Unverified
#ModelMetricClaimedVerifiedStatus
1HWTSC-Teacher-SimScore19.97Unverified
2MS-COMET-22Score19.89Unverified
3MS-COMET-QE-22Score19.76Unverified
4KG-BERTScoreScore17.28Unverified
5metricx_xl_DA_2019Score17.17Unverified
6COMET-QEScore16.8Unverified
7COMET-22Score16.31Unverified
8UniTE-srcScore15.68Unverified
9UniTE-refScore15.38Unverified
10metricx_xxl_DA_2019Score15.24Unverified