https://gitlab.com/skalo/baroque-nmt
공식 코드Modeling Baroque Two-Part Counterpoint with Neural Machine Translation
Papers with Code 아카이브
Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 84
- 보존된 평가 기록
- 438
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://huggingface.co/datasets/MLCommons/speech-wikimedia
공식 코드https://huggingface.co/disk0dancer/ruBert-base-finetuned-pos
공식 코드POS-tagging to highlight the skeletal structure of sentences
https://huggingface.co/naist-nlp/mitre_466m
공식 코드Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation
https://huggingface.co/naist-nlp/mitre_913m
공식 코드Registering Source Tokens to Target Language Spaces in Multilingual Neural Machine Translation
https://zenodo.org/record/10500594
공식 코드https://github.com/04RR/SOTA-Vision
https://github.com/1-punchMan/CLTS
https://github.com/10-zin/Synthesizer
Synthesizer: Rethinking Self-Attention in Transformer Models
https://github.com/1alexandra/speech
Attention-Based Models for Text-Dependent Speaker Verification
https://github.com/1iyc/T2T-Analysis
https://github.com/1wy/bert
https://github.com/1ytic/warp-rnnt
https://github.com/201419/Optimizer-PyTorch
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/GRU
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Transformer
https://github.com/2023-MindSpore-1/ms-code-149
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-169
https://github.com/2023-MindSpore-1/ms-code-197
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/2023-MindSpore-1/ms-code-198
https://github.com/2023-MindSpore-1/ms-code-200
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/gnmt_v2
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/2023-MindSpore-1/ms-code-53
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/2023-MindSpore-1/ms-code-61
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/2023-MindSpore-1/ms-code-62
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/Unet3d
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_08_recurrent-neural-networks
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
https://github.com/2023-MindSpore-4/Code8/tree/main/AttentionCluster
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/audio_spectrogram_transformer
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bart
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/bert_generation
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/fnet
https://github.com/23LuZ/the-Evaluation-of-ChitChat-System
https://github.com/2miatran/Natural-Language-Processing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.