https://github.com/bkoch4142/attention-is-all-you-need-paper
Neural Machine Translation by Jointly Learning to Align and Translate
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/black0017/MedicalZooPytorch
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/blazejdolicki/multilingual-analysis
https://github.com/blickwinkel1107/making-the-most-of-context-nmt
Towards Making the Most of Context in Neural Machine Translation
https://github.com/bloomsburyai/question-generation
https://github.com/bluejurand/Kaggle_QA_Google_Labeling
https://github.com/bminixhofer/nnsplit
Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
https://github.com/bojone/keras_lookahead
https://github.com/bojone/r-drop
https://github.com/bond005/yandex-shifts-weather
https://github.com/boom85423/Seq2seq-model-for-Meme-Generator
https://github.com/booydar/t5-experiments
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/bplank/teaching-dl4nlp
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/brainsqueeze/text2vec
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/bramiozo/PubScience
BVS Corpus: A Multilingual Parallel Corpus of Biomedical Scientific Texts
https://github.com/brandontrabucco/scaled_dot_product_attention
https://github.com/brightmart/bert_customized
https://github.com/brightmart/bert_original
https://github.com/brightmart/text_classification
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/brynnchernosky/seq2seq
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
https://github.com/bshantam97/Attention_Based_Networks
https://github.com/btobab/R-Drop
https://github.com/burcgokden/PLDR-LLM-with-KVG-cache
Power Law Graph Transformer for Machine Translation and Representation Learning
https://github.com/burcgokden/llm-from-power-law-decoder-representations
Power Law Graph Transformer for Machine Translation and Representation Learning
https://github.com/butsugiri/shape
https://github.com/bywords/lang-style-transfer-legacy
https://github.com/bzhangGo/lrn
https://github.com/bzylich/persona-chatbot
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/c00k1ez/plain-transformers
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/cadia-lvl/fastspeech2
https://github.com/caesium94/seq2seq_fix
Massive Exploration of Neural Machine Translation Architectures
https://github.com/cahya-wirawan/indonesian-language-models
https://github.com/cairouchong/discourse-phenomena
Pronoun Translation in English-French Machine Translation: An Analysis of Error Types
https://github.com/calclavia/NOS
https://github.com/cambridgeltl/composable-sft
NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages
https://github.com/camlaedtke/NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.