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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/Kevin-Vora/bert-embedding-gluonnlp-edit-

https://github.com/KevinYoung98/Seq2Seq-PyTorch

https://github.com/Khalid-Jamal/annotated-transformer-Arabic

https://github.com/Kirankumaraswamy/Transformers

https://github.com/KrishnaTarun/Deep-Learning-Lab

https://github.com/Kyubyong/transformer

https://github.com/Lemoners/Attention-Is-All-You-Need

https://github.com/LeoWood/bert-horovod

https://github.com/LiamMaclean216/Pytorch-Chatbot

https://github.com/LiamMaclean216/Pytorch-Transfomer

https://github.com/LivXue/MMT

https://github.com/LoveYang/bert_test

https://github.com/Lsdefine/attention-is-all-you-need-keras

https://github.com/MCLYang/RhythmGAN_pytorch

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

https://github.com/MS-P3/code4/tree/main/TAT

https://github.com/MaZhiyuanBUAA/bert-tf1.4.0

https://github.com/Maple728/transformer

https://github.com/MarcBS/keras

https://github.com/MashaKubyshina/learning_ML

https://github.com/Matthewdowney18/Transformer_Dialogue

https://github.com/Maz101/Bert

https://github.com/Maz101/pretrainingdataset

https://github.com/MichaelZhouwang/LMlexsub

https://github.com/MindCode-4/code-5/tree/main/transformer

https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model

https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model

https://github.com/MirunaPislar/multi-head-attention-labeller

https://github.com/Misoknisky/Bert-MultiGpu

https://github.com/MohamedAbdelsalam9/TT-Transformer

https://github.com/NVIDIA/sentiment-discovery

https://github.com/Neoanarika/torchexplainer

https://github.com/Nimesh-Patel/BERT

https://github.com/Nstats/bert_MRC

https://github.com/Nstats/bert_senti_analysis_ch

https://github.com/Nstats/my_bert