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 10501–10550 of 10752 papers

TitleStatusHype
We Need to Talk About Classification Evaluation Metrics in NLP—0
WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition—0
Were the clocks striking or surprising? Using WSD to improve MT performance—0
The Impact of Preprocessing on Arabic-English Statistical and Neural Machine Translation—0
WeTS: A Benchmark for Translation Suggestion—0
What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns—0
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation—0
YiSi - a Unified Semantic MT Quality Evaluation and Estimation Metric for Languages with Different Levels of Available Resources—0
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension—0
What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects—0
What does Attention in Neural Machine Translation Pay Attention to?—0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension—0
The Impact of Multiword Expression Compositionality on Machine Translation Evaluation—0
What do RNN Language Models Learn about Filler--Gap Dependencies?—0
What Do You Get When You Cross Beam Search with Nucleus Sampling?—0
What good are `Nominalkomposita' for `noun compounds': Multilingual Extraction and Structure Analysis of Nominal Compositions using Linguistic Restrictors—0
What is Hidden among Translation Rules—0
What is it? Disambiguating the different readings of the pronoun `it'—0
The Impact of Model Scaling on Seen and Unseen Language Performance—0
What is the Best Way for ChatGPT to Translate Poetry?—0
What Level of Quality can Neural Machine Translation Attain on Literary Text?—0
Could We Have Had Better Multilingual LLMs If English Was Not the Central Language?—0
What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation—0
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation—0
What Matters Most in Morphologically Segmented SMT Models?—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
YODA System for WMT16 Shared Task: Bilingual Document Alignment—0
You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation—0
What's in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation—0
What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation—0
What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT—0
What’s the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT—0
You Need to Pay Better Attention: Rethinking the Mathematics of Attention Mechanism—0
What we need to learn if we want to do and not just talk—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation—0
What you can cram into a single \$\&!\#* vector: Probing sentence embeddings for linguistic properties—0
The Impact of Machine Translation Quality on Human Post-Editing—0
When and why are log-linear models self-normalizing?—0
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One—0
``You Sound Just Like Your Father'' Commercial Machine Translation Systems Include Stylistic Biases—0
When and Why is Unsupervised Neural Machine Translation Useless?—0
The Impact of Indirect Machine Translation on Sentiment Classification—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
You’ve translated it, now what?—0
When does deep multi-task learning work for loosely related document classification tasks?—0
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale—0
When does Parameter-Efficient Transfer Learning Work for Machine Translation?—0
YSDA Participation in the WMT'16 Quality Estimation Shared Task—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