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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/yash-nishaant/Seq2Seq-Chatbot
https://github.com/yashbonde/RNN-sim
https://github.com/yasufumy/pytorch_lstm_jump
https://github.com/ye-kyaw-thu/myword
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
https://github.com/yeeeqichen/Bert
https://github.com/yeyinthtoon/tf2-resmlp
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/yikangshen/megablocks
Mixture of Attention Heads: Selecting Attention Heads Per Token
https://github.com/yinghao1019/NLP_and_DL_practice
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/yinghao1019/NLP_and_DL_practice/blob/master/Conv_seq2seq.ipynb
https://github.com/yiyc-kor/bert-study
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ykrmm/ICLR_2020
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ykrmm/TREMBA
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/ymoslem/opennmt-tutorial
Fine-tuning Large Language Models for Adaptive Machine Translation
https://github.com/yongbowin/san_mrc_annotation
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/yoonkim/lstm-char-cnn
Compositional Morphology for Word Representations and Language Modelling
https://github.com/youichiro/transformer-copy
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/yscope75/CS2225.CH2001020
https://github.com/yuanmu97/secure-transformer-inference
https://github.com/yuantiku/fairseq-gec
https://github.com/yuekai146/NMT
Revisiting Low-Resource Neural Machine Translation: A Case Study
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuhangT/tf_bert
https://github.com/yuhui-zh15/nlg_metrics
https://github.com/yukio326/gan-nmt
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
https://github.com/yukio326/reconstructor-nmt
English-Japanese Neural Machine Translation with Encoder-Decoder-Reconstructor
https://github.com/yumoh/speech-keras
https://github.com/yunsukim86/sockeye-noise
https://github.com/yunsukim86/wbw-lm
https://github.com/yurakuratov/t5-experiments
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/yurayli/stanford-cs224n-sol
Neural Machine Translation by Jointly Learning to Align and Translate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.