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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 151–200 of 10752 papers

TitleStatusHype
Do Language Models Understand Honorific Systems in Javanese?—0
Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin?—0
Connecting the Persian-speaking World through Transliteration—0
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning—0
Alleviating Distribution Shift in Synthetic Data for Machine Translation Quality Estimation—0
Evaluating Gender Bias in German Machine TranslationCode0
Enhancing Human Evaluation in Machine Translation with Comparative Judgment—0
UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings—0
Using Machine Learning to Detect Fraudulent SMSs in Chichewa—0
Automatic Input Rewriting Improves Translation with Large Language ModelsCode1
LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models—0
Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMsCode1
Early-Exit and Instant Confidence Translation Quality EstimationCode0
Effects of Prompt Length on Domain-specific Tasks for Large Language Models—0
English Please: Evaluating Machine Translation with Large Language Models for Multilingual Bug ReportsCode0
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality—0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions—0
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & DialectsCode2
How Much Do LLMs Hallucinate across Languages? On Multilingual Estimation of LLM Hallucination in the WildCode0
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral—0
LMN: A Tool for Generating Machine Enforceable Policies from Natural Language Access Control Rules using LLMs—0
Efficient Machine Translation Corpus Generation: Integrating Human-in-the-Loop Post-Editing with Large Language ModelsCode0
Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis—0
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on ManchuCode1
Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks—0
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation—0
TUMLU: A Unified and Native Language Understanding Benchmark for Turkic LanguagesCode1
ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition—0
Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive SurveyCode0
Truth Knows No Language: Evaluating Truthfulness Beyond EnglishCode0
Unsupervised Translation of Emergent Communication—0
Evaluating Text Style Transfer Evaluation: Are There Any Reliable Metrics?—0
Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and ConsistencyCode0
BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation—0
Multilingual Non-Autoregressive Machine Translation without Knowledge DistillationCode0
DOLFIN -- Document-Level Financial test set for Machine Translation—0
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study—0
A comparison of translation performance between DeepL and SupertextCode0
Beyond English: Evaluating Automated Measurement of Moral Foundations in Non-English Discourse with a Chinese Case StudyCode0
When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation—0
An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese—0
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected—0
How to Select Datapoints for Efficient Human Evaluation of NLG Models?Code1
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation—0
Cross-Language Approach for Quranic QA—0
Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization—0
Misspellings in Natural Language Processing: A survey—0
Few-Shot Optimized Framework for Hallucination Detection in Resource-Limited NLP Systems—0
DialUp! Modeling the Language Continuum by Adapting Models to Dialects and Dialects to 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