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Data-to-Text Generation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 3
평가표: 26
보존된 평가 기록: 122

A classic problem in natural-language generation (NLG) involves taking structured data, such as a table, as input, and producing text that adequately and fluently describes this data as output. Unlike machine translation, which aims for complete transduction of the sentence to be translated, this form of NLG is usually taken to require addressing (at least) two separate challenges: what to say, the selection of an appropriate subset of the input data to discuss, and how to say it, the surface realization of a generation.

<span style="color:grey; opacity: 0.6">( Image credit: Data-to-Text Generation with Content Selection and Planning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

Data-to-Text Generation

코드

https://github.com/ahmetumutdurmus/awd-lstm

https://github.com/ahsanabbas123/Transformer

https://github.com/akanyaani/minGPTF

https://github.com/akashe/Python-Code-Generation

https://github.com/algharak/BERTenhance

https://github.com/alibaba-damo-academy/FunASR

https://github.com/alibabasglab/frcrn

https://github.com/alililia/ms_contrib/tree/main/ascend_transformer

https://github.com/alililia/ms_contrib/tree/main/gpu_transformer

https://github.com/alimpiev/embeddings_persistent_homology

https://github.com/amitz25/PCCoder

https://github.com/anhinga/JuliaCon2021-poster

https://github.com/anhtu293/transformer_from_scratch

https://github.com/anirudharc/transformer

https://github.com/anishnarkar/experiments_deeplearning_models

https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION

https://github.com/antoinecollas/transformer_neural_machine_translation

https://github.com/anton-bushuiev/ppiformer

https://github.com/anuscode/nrms

https://github.com/appcoreopc/berty

https://github.com/arkel23/PyTorch-Pretrained-ViT

https://github.com/asfathermou/human-computer-interaction

https://github.com/asultan123/DL_LLC_Prefetching

https://github.com/augustgw/early-exit-transformer

https://github.com/autobotasia/vibert

https://github.com/awslabs/sockeye

https://github.com/ays-dev/keras-transformer

https://github.com/b-etienne/Seq2seq-PyTorch

https://github.com/bangoc123/transformer

https://github.com/baosongyang/WRD

https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT

https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT

https://github.com/bartzi/chainer-transformer

https://github.com/basile-bron/Artificial-Intelligence-Lexical

https://github.com/bcmi220/esc4nmt

https://github.com/bdusell/stack-attention