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 801–850 of 10752 papers

TitleStatusHype
Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine TranslationCode0
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models—0
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation—0
Multi-objective Differentiable Neural Architecture SearchCode1
Fine-Tuned Machine Translation Metrics Struggle in Unseen DomainsCode0
XMoE: Sparse Models with Fine-grained and Adaptive Expert SelectionCode1
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method—0
Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic SupervisionCode0
TEaR: Improving LLM-based Machine Translation with Systematic Self-RefinementCode2
Layer-wise Regularized Dropout for Neural Language Models—0
Training a Bilingual Language Model by Mapping Tokens onto a Shared Character Space—0
TMT: Tri-Modal Translation between Speech, Image, and Text by Processing Different Modalities as Different LanguagesCode1
Fine-tuning Large Language Models for Domain-specific Machine Translation—0
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware TranslatorsCode0
Transformers are Expressive, But Are They Expressive Enough for Regression?Code0
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages—0
Two Counterexamples to Tokenization and the Noiseless Channel—0
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer—0
Could We Have Had Better Multilingual LLMs If English Was Not the Central Language?—0
Bangla AI: A Framework for Machine Translation Utilizing Large Language Models for Ethnic Media—0
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures—0
RefuteBench: Evaluating Refuting Instruction-Following for Large Language ModelsCode0
Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation—0
SiLLM: Large Language Models for Simultaneous Machine TranslationCode1
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translationCode0
Asynchronous and Segmented Bidirectional Encoding for NMT—0
Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based MetricCode0
Analysis of Levenshtein Transformer's Decoder and Its Variants—0
NEO-BENCH: Evaluating Robustness of Large Language Models with NeologismsCode0
What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects—0
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization—0
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution—0
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models—0
Rethinking Human-like Translation Strategy: Integrating Drift-Diffusion Model with Large Language Models for Machine Translation—0
Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularization—0
Self-Augmented In-Context Learning for Unsupervised Word TranslationCode0
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation—0
Generating Diverse Translation with Perturbed kNN-MT—0
Unsupervised Sign Language Translation and Generation—0
Large Language Models "Ad Referendum": How Good Are They at Machine Translation in the Legal Domain?—0
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel CorporaCode0
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine TranslatorsCode2
Inducing Systematicity in Transformers by Attending to Structurally Quantized EmbeddingsCode1
Promoting Target Data in Context-aware Neural Machine Translation—0
A Prompt Response to the Demand for Automatic Gender-Neutral TranslationCode0
Neural machine translation of clinical procedure codes for medical diagnosis and uncertainty quantification—0
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach—0
TransLLaMa: LLM-based Simultaneous Translation SystemCode1
The Potential of AutoML for Recommender Systems—0
Scaling Laws for Downstream Task Performance of Large Language Models—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