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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/rileynwong/pytorch-seq2seq-joke2punchline
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/rishikksh20/ResMLP-pytorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/rishikksh20/rectified-linear-attention
https://github.com/riversdie/chatbot
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/roberttwomey/machine-imagination-isea
https://github.com/roberttwomey/machine-imagination-workshop
https://github.com/rodgzilla/machine_learning_deep_knn
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/rohan400/PepperQA
https://github.com/rojagtap/abstractive_summarizer
https://github.com/romankoshkin/transllama
https://github.com/rongjiehuang/transpeech
https://github.com/roomylee/rnn-text-classification-tf
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/rpuiggari/bert2
https://github.com/ruiqi-zhong/acl2021-instance-level
https://github.com/ruiyiw/VT-summ
https://github.com/ruoyuxie/interpretable_dialect_classifier
FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation
https://github.com/rupakdas18/Research-Project-UMD-CS-5642
https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
https://github.com/rupakdas18/SuperGlue-tasks-using-BERT
https://github.com/russiannlp/rucola
Multilingual Translation with Extensible Multilingual Pretraining and Finetuning
https://github.com/rvandewater/yaib
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/rwightman/pytorch-image-models
https://github.com/rxn4chemistry/OpenNMT-py
https://github.com/s-ankur/fairseq-gec
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/s1879281/Lexical-NMT
https://github.com/s3prl/End-to-end-ASR-Pytorch
https://github.com/s9891326/Fine-Tuning-BERT
https://github.com/sabetAI/bucc-eval
https://github.com/sachin19/mucoco
Controlled Text Generation as Continuous Optimization with Multiple Constraints
https://github.com/safakkbilici/Academic-Paper-Title-Recommendation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/safpla/AutoLossRelease
AutoLoss: Learning Discrete Schedules for Alternate Optimization
https://github.com/sainimohit23/hindi-stemmer
https://github.com/saisandeep97/Chat-botV2
Effective Approaches to Attention-based Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.