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 2601–2650 of 10752 papers

TitleStatusHype
How well do real-time machine translation apps perform in practice? Insights from a literature review—0
Building Domain-specific Corpora from the Web: the Case of European Digital Service Infrastructures—0
Building Comparable Corpora for Assessing Multi-Word Term Alignment—0
LiSTra Automatic Speech Translation: English to Lingala Case Study—0
Multi-Domain Adaptation in Neural Machine Translation with Dynamic Sampling Strategies—0
DeepSPIN: Deep Structured Prediction for Natural Language Processing—0
Error Annotation in Post-Editing Machine Translation: Investigating the Impact of Text-to-Speech Technology—0
On the Interaction of Regularization Factors in Low-resource Neural Machine TranslationCode0
The EuroPat Corpus: A Parallel Corpus of European Patent Data—0
A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content—0
Agent and User-Generated Content and its Impact on Customer Support MT—0
Europeana Translate: Providing multilingual access to digital cultural heritage—0
Encouraging Neural Machine Translation to Satisfy Terminology Constraints.—0
Flux d’informations dans les systèmes encodeur-décodeur. Application à l’explication des biais de genre dans les systèmes de traduction automatique. (Information flow in encoder-decoder systems applied to the explanation of gender bias in machine translation systems)—0
Language Planning in Action: Depiction as a Driver of New Terminology in Irish Sign Language—0
DiHuTra: a Parallel Corpus to Analyse Differences between Human TranslationsCode0
Investigating automatic and manual filtering methods to produce MT-ready glossaries from existing ones—0
MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set—0
Post-editing in Automatic Subtitling: A Subtitlers’ perspectiveCode0
A First Corpus of AZee Discourse Expressions—0
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation—0
Rethinking the Design of Sequence-to-Sequence Models for Efficient Machine Translation—0
Developing Machine Translation Engines for Multilingual Participatory Spaces—0
Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation—0
Mean Machine Translations: On Gender Bias in Icelandic Machine Translations—0
Neural Speech Translation: From Neural Machine Translation to Direct Speech Translation—0
Studying Post-Editese in a Professional Context: A Pilot Study—0
Unsupervised Machine Translation in Real-World Scenarios—0
GoURMET – Machine Translation for Low-Resourced Languages—0
Introducing EM-FT for Manipuri-English Neural Machine Translation—0
On “Human Parity” and “Super Human Performance” in Machine Translation Evaluation—0
DeBiasByUs: Raising Awareness and Creating a Database of MT Bias—0
Priming Ancient Korean Neural Machine Translation—0
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing—0
Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer ContextCode0
The use of online translators by students not enrolled in a professional translation program: beyond copying and pasting for a professional use—0
SciPar: A Collection of Parallel Corpora from Scientific Abstracts—0
Sign Language Translation: Ongoing Development, Challenges and Innovations in the SignON Project—0
MTee: Open Machine Translation Platform for Estonian Government—0
Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components—0
Visual Transformer for Object Detection—0
Automatic Bilingual Phrase Dictionary Construction from GIZA++ Output—0
Automatically extracting the semantic network out of public services to support cities becoming Smart Cities—0
CUNI Submission to the BUCC 2022 Shared Task on Bilingual Term Alignment—0
CUNI Submission to MT4All Shared Task—0
KC4MT: A High-Quality Corpus for Multilingual Machine Translation—0
Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars—0
Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch—0
Passing Parser Uncertainty to the Transformer. Labeled Dependency Distributions for Neural Machine Translation.—0
QUARTZ: Quality-Aware Machine Translation—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