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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/gooofy/zbrain

https://github.com/gooppe/transformer-summarization

https://github.com/gordicaleksa/pytorch-original-transformer

https://github.com/graykode/gpt-2-Pytorch

https://github.com/graykode/nlp-tutorial

https://github.com/guihuzhang/FactSpotter

https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network

https://github.com/guoyaohua/BERT-Chinese-Annotation

https://github.com/guzhang480/Google_BERT

https://github.com/h4ste/oscar

https://github.com/haidershaour/bert

https://github.com/halo090770/bert

https://github.com/han-shi/SparseBERT

https://github.com/hannibal046/nanorwkv

https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning

https://github.com/harshit158/paper-dots

https://github.com/harvardnlp/annotated-transformer

https://github.com/hfxunlp/transformer

https://github.com/hiun/learning-transformers

https://github.com/hiyouga/llama-efficient-tuning

https://github.com/hkust-longgroup/comm

https://github.com/hnt4499/seq2seq

https://github.com/hoangtrungchinh/Bert-SQuAD-v2

https://github.com/hrbigelow/transformer-aiayn

https://github.com/hubiodatalab/druggen

https://github.com/huggingface/transformers

https://github.com/hugochan/Graph2Seq-for-KGQG

https://github.com/huulinhcvp/chatBot

https://github.com/hyunwookl/testam

https://github.com/hyunwoongko/transformer

https://github.com/icewing1996/bert_dep

https://github.com/ictnlp/DiverseNMT

https://github.com/ictnlp/NA-MNMT

https://github.com/ictnlp/PTE-NMT

https://github.com/ictnlp/TLAT-NMT

https://github.com/idiotprofessorchen/bert.github.io