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 7151–7200 of 10752 papers

TitleStatusHype
Improving Wordnets for Under-Resourced Languages Using Machine Translation—0
Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings—0
Improving Word Sense Disambiguation in Neural Machine Translation with Salient Document Context—0
Improving Word Sense Disambiguation with Translations—0
Improving Word Translation Disambiguation by Capturing Multiword Expressions with Dictionaries—0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
Improving Zero-shot Translation with Language-Independent Constraints—0
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
IMS’ Systems for the IWSLT 2021 Low-Resource Speech Translation Task—0
INarIG: Iterative Non-autoregressive Instruct Generation Model For Word-Level Auto Completion—0
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar—0
In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models—0
Incorporate Semantic Structures into Machine Translation Evaluation via UCCA—0
Incorporating Alternate Translations into English Translation Treebank—0
Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation—0
Incorporating External Annotation to improve Named Entity Translation in NMT—0
Incorporating Global Visual Features into Attention-Based Neural Machine Translation—0
Incorporating Global Visual Features into Attention-based Neural Machine Translation.—0
Incorporating Human Translator Style into English-Turkish Literary Machine Translation—0
Incorporating Linguistic Knowledge in Statistical Machine Translation: Translating Prepositions—0
Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Incorporating Side Information into Recurrent Neural Network Language Models—0
Incorporating Source-Side Phrase Structures into Neural Machine Translation—0
Incorporating Source Syntax into Transformer-Based Neural Machine Translation—0
Incorporating Structural Alignment Biases into an Attentional Neural Translation Model—0
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model—0
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model—0
Incorporating translation quality estimation into Chinese-Korean neural machine translation—0
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring—0
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation—0
Incorporation of a valency lexicon into a TectoMT pipeline—0
Increasing Coverage of Translation Memories with Linguistically Motivated Segment Combination Methods—0
Increasing the throughput of machine translation systems using clouds—0
Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective—0
Incremental Adaptation of NMT for Professional Post-editors: A User Study—0
Incremental Adaptation Strategies for Neural Network Language Models—0
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation—0
Incremental Derivations in CCG—0
Incrementally Updating the SMT Reordering Model—0
Incremental Prediction of Sentence-final Verbs: Humans versus Machines—0
Incremental Segmentation and Decoding Strategies for Simultaneous Translation—0
Incremental Semantic Role Labeling with Tree Adjoining Grammar—0
Incremental Topic-Based Translation Model Adaptation for Conversational Spoken Language Translation—0
Incremental Tree Substitution Grammar for Parsing and Sentence Prediction—0
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation—0
`Indicatements' that character language models learn English morpho-syntactic units and regularities—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages—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