https://github.com/AshwinDeshpande96/Hierarchical-Softmax
Strategies for Training Large Vocabulary Neural Language Models
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/Aso-UniMelb/neural-fieldwork-guide
https://github.com/Aspire-Mayank/Top-Deep-Learning-Stuffs
https://github.com/AssafSinger94/sigmorphon-2020-inflection
https://github.com/AtluriNikhil/Image-Captioning
https://github.com/Automattic/wp-translate
https://github.com/Aveek-Saha/Transformer
https://github.com/Avmb/lowrank-gru
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ResMLP
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/Babylonpartners/MultilingualFactorAnalysis
https://github.com/Babylonpartners/fastText_multilingual
Exploiting Similarities among Languages for Machine Translation
https://github.com/Baichenjia/NMT-eager
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Baichenjia/Tensorflow-TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/BangLiu/QANet-PyTorch
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/BasileBron/Artificial-Intelligence-Lexical
https://github.com/Belval/NRTR
https://github.com/Bhavnicksm/vanilla-transformer-jax
https://github.com/BigBinnie/Benchmarking-LLM-based-Machine-Translation-on-Cultural-Awareness
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/CGCL-codes/naturalcc
https://github.com/CKPOON0619/Kaggle2Sigma
https://github.com/CKRC24/Listen-and-Translate
https://github.com/COMP6248-Reproducability-Challenge/Differentiables_FastAccurateTextClassification
https://github.com/COMP6248-Reproducability-Challenge/LookaheadOptimizer
https://github.com/CSTR-Edinburgh/ophelia
https://github.com/CVxTz/music_genre_classification
https://github.com/CZWin32768/seqmnist
https://github.com/Caesarzhang/bert-zh
https://github.com/CatherineWong/dancin_seq2seq
TextBugger: Generating Adversarial Text Against Real-world Applications
https://github.com/Cenrax/Image-Captioning-with-Translation-
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ChaiBapchya/mxnet-hardmax_machine_translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch
https://github.com/Charliesgithub20221030/BERT
https://github.com/Chatha-Sphere/pno-ai
https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Chinnu1103/Machine-Translation-using-Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.