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/davidmrau/mixture-of-experts
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/davidsvaughn/dts-tf
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/davisking/dlib-models
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dayyass/neural-machine-translation
https://github.com/dccuchile/CC6204
https://github.com/deep-spin/OpenNMT-entmax
https://github.com/deep-spin/quati
https://github.com/deep-spin/sigmorphon-seq2seq
https://github.com/deepmind/haiku
https://github.com/deepmipt/bert/tree/feat/multi_gpu
https://github.com/deepqmc/deepqmc
https://github.com/deepsense-ai/unblackboxing_webinar
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/demegire/Parameterization-of-Hypercomplex-Multiplications
https://github.com/demelin/nematode
https://github.com/denizyuret/fastsubs-googlecode
https://github.com/derylucio/Transformer
https://github.com/deterministic-algorithms-lab/Large-XLM
https://github.com/dewanderelex/LanguageTranslation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/dg-enlens/banet-depth-prediction
Bidirectional Attention Network for Monocular Depth Estimation
https://github.com/dhecloud/simple_language_modelling
https://github.com/dhiraa/tener
https://github.com/dhruvdcoder/sparse-structured-attention
A Regularized Framework for Sparse and Structured Neural Attention
https://github.com/dhruvramani/Transformers-RL
https://github.com/dice-group/KG-NMT
https://github.com/distractor-generation/dg_survey
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/dksifoua/Neural-Machine-Translation
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/dl-nlp/dl-nlp.github.io
https://github.com/dltcls/g2p_pytorch_wiktionary
https://github.com/dmeoli/neuro-sat
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero
https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
https://github.com/dmlc/gluon-nlp
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/dongdong199408/teachchatrobot
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/dreamgonfly/Transformer-pytorch
https://github.com/dreamnotover/english_chinese_machine_translation_baseline
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.