https://github.com/ufal/nmt-pe-effects-2021
Neural Machine Translation Quality and Post-Editing Performance
Papers with Code 아카이브
Machine Translation
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/uhh-lt/amharicmodels
The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation
https://github.com/ukplab/acl2022-impli
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms
https://github.com/ukplab/arxiv2025-inherent-limits-plms
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/umeiko/mindspore-seq2seq
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/umutguneri/Question-Answering-Assistant
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/unbabel/mt-telescope
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers
https://github.com/unconst/MACH
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/urvashik/knnmt
https://github.com/utanaka2000/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/uzi0espil/research-papers-implementation/tree/master/Effective%20Approaches%20to%20Attention-based%20Neural%20Machine%20Translation
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/uzi0espil/research-papers-implementation/tree/master/Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vGkatsis/Chat_Bot_DL
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/vadimkantorov/ctc
A Stable and Effective Learning Strategy for Trainable Greedy Decoding
https://github.com/valentinmace/noisy-text
https://github.com/vanderpoelliam/cpmi
Detecting Hallucinated Content in Conditional Neural Sequence Generation
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varenick/manifold_latent_vae
Embedding-reparameterization procedure for manifold-valued latent variables in generative models
https://github.com/varun-bhaseen/Image-caption-generation-using-attention-model
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vchopin/GNMT_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/veekaybee/what_are_embeddings
https://github.com/vene/sparse-structured-attention
A Regularized Framework for Sparse and Structured Neural Attention
https://github.com/venkat-ravilla/DAP
Guiding attention in Sequence-to-sequence models for Dialogue Act prediction
https://github.com/vergilus/NJUNMT-pytorch/tree/master/adversarials
A Reinforced Generation of Adversarial Examples for Neural Machine Translation
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vietai/SAT
https://github.com/vijay120/bert
https://github.com/vikashranjan/transformer
https://github.com/vikua/keras-attention-models
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/vilm-ai/viet-llm-eval
https://github.com/vinairesearch/phogpt
https://github.com/vinayak19th/Brevis-2.0
https://github.com/vinayprabhu/X-is-all-you-need
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.