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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 3051–3100 of 10752 papers

TitleStatusHype
Low-Resource Machine Translation based on Asynchronous Dynamic Programming—0
基于模型不确定性约束的半监督汉缅神经机器翻译(Semi-Supervised Chinese-Myanmar Neural Machine Translation based Model-Uncertainty)—0
基于层间知识蒸馏的神经机器翻译(Inter-layer Knowledge Distillation for Neural Machine Translation)—0
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method—0
融合XLM词语表示的神经机器译文自动评价方法(Neural Automatic Evaluation of Machine Translation Method Combined with XLM Word Representation)—0
融合零指代识别的篇章级机器翻译(Context-aware Machine Translation Integrating Zero Pronoun Recognition)—0
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation—0
Machine Translation with Pre-specified Target-side Words Using a Semi-autoregressive Model—0
Language Relatedness and Lexical Closeness can help Improve Multilingual NMT: IITBombay@MultiIndicNMT WAT2021—0
NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021—0
NICT’s Neural Machine Translation Systems for the WAT21 Restricted Translation Task—0
Hybrid Statistical Machine Translation for English-Myanmar: UTYCC Submission to WAT-2021—0
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task—0
Multilingual Machine Translation Systems at WAT 2021: One-to-Many and Many-to-One Transformer based NMT—0
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text—0
NECTEC’s Participation in WAT-2021—0
Improved English to Hindi Multimodal Neural Machine Translation—0
TMU NMT System with Japanese BART for the Patent task of WAT 2021—0
Textual Representations for Crosslingual Information Retrieval—0
TMEKU System for the WAT2021 Multimodal Translation Task—0
IITP-MT at WAT2021: Indic-English Multilingual Neural Machine Translation using Romanized Vocabulary—0
Optimal Word Segmentation for Neural Machine Translation into Dravidian Languages—0
CommitBERT: Commit Message Generation Using Pre-Trained Programming Language ModelCode1
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation—0
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation—0
Mid-Air Hand Gestures for Post-Editing of Machine TranslationCode0
Pre-training Methods for Neural Machine Translation—0
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training—0
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text GenerationCode0
LZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding—0
Beyond Sentence-Level End-to-End Speech Translation: Context Helps—0
Transformer-Based Direct Hidden Markov Model for Machine Translation—0
Discriminative Reranking for Neural Machine Translation—0
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation—0
Edit Distance Based Curriculum Learning for Paraphrase Generation—0
Multilingual Agreement for Multilingual Neural Machine Translation—0
Video-guided Machine Translation with Spatial Hierarchical Attention Network—0
MT-Telescope: An interactive platform for contrastive evaluation of MT systems—0
Neural Machine Translation with Synchronous Latent Phrase Structure—0
Meta Learning and Its Applications to Natural Language Processing—0
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?—0
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena—0
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages—0
Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation—0
Crafting Adversarial Examples for Neural Machine Translation—0
Attention Calibration for Transformer in Neural Machine Translation—0
Lexicon Learning for Few Shot Sequence ModelingCode1
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation—0
A Bidirectional Transformer Based Alignment Model for Unsupervised Word Alignment—0
Continuous Language Generative FlowCode1
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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