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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/neqkir/attention-mechanism

https://github.com/ngocphucck/BERT-questions-classification

https://github.com/niccolot/ENG-ITA_NMT

https://github.com/nicholasbao/nlp_job

https://github.com/nietao2/DeepSpeechRecognition

https://github.com/nlinc1905/dsilt-tsa

https://github.com/nmfisher/bert-modified

https://github.com/noriyukipy/tfchat

https://github.com/noriyukipy/tfdlg

https://github.com/nrc-cnrc/sockeye-multisource

https://github.com/nsfzyzz/generalization_metrics_for_nlp

https://github.com/nvshrao/Bidirectional-Transformer

https://github.com/okkteam/Transformer-Transducer

https://github.com/ololo123321/maestro

https://github.com/open-mmlab/mmclassification

https://github.com/opennmt/ctranslate2

https://github.com/owainwest/uniprot_bert

https://github.com/paolanu/BERT_epitope

https://github.com/parcorelab/comscribe

https://github.com/patrickhart/jaxdl

https://github.com/paulskeie/stadnamn

https://github.com/pbcquoc/transformer

https://github.com/pcyin/pytorch_basic_nmt

https://github.com/pengbaolin/soloist

https://github.com/pengming617/bert_classification

https://github.com/pengshuyuan/Bert

https://github.com/penny9287/sockeye

https://github.com/pennylaneai/generative-quantum-states

https://github.com/philipperemy/keras-attention-mechanism

https://github.com/phohenecker/pytorch-transformer

https://github.com/pingheng001/Cnn-Bert

https://github.com/piquil/rydberggpt

https://github.com/plkmo/Transformer-Eng2French

https://github.com/pomonam/AttentionCluster

https://github.com/prabhupad26/100daysofML

https://github.com/prachetaphadnis/NMT_Seq2Seq_LexicalModel