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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/EmilioTyl/transformers_from_scratch

https://github.com/Enigmatisms/Set-Transformer

https://github.com/EthanWYB/bert-classification

https://github.com/EugeneSel/EUMT

https://github.com/EuphoriaYan/bert_component

https://github.com/FaskyCC/TextClassification

https://github.com/FengJiaChunFromSYSU/Bert

https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq

https://github.com/Fuchai/language

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction

https://github.com/G-4-R-Y/Tweet-Sentiment-Extraction-roBERTa-5fold

https://github.com/Gaozhen0816/BERT_QA_For_AILaw

https://github.com/Gaozhen0816/BERT_QA_for_Chinese

https://github.com/Glaceon31/Document-Transformer

https://github.com/GlassyWing/transformer-keras

https://github.com/HKUST-KnowComp/R-Net

https://github.com/HanhaiNotHai/transformer

https://github.com/Harry-Muzart/harry-muzart.github.io

https://github.com/Helsinki-NLP/shared-info

https://github.com/HeshamElAbd/SelfAttentionLangModel

https://github.com/HzcIrving/DeepLearning_PlayGround

https://github.com/IBM/pytorch-seq2seq

https://github.com/IOB-Muenster/TEclass2

https://github.com/IrishCoffee/cudnnMultiHeadAttention

https://github.com/Iwontbecreative/Abstractive-summarization-OpenNMT

https://github.com/Izecson/saml-nmt

https://github.com/Izecson/sockeye-1.16.6

https://github.com/JNUpython/bert

https://github.com/JRC1995/Machine-Translation-Transformers

https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer

https://github.com/JeheonPark596/sentimentsurvival

https://github.com/JohannLee1996/bert

https://github.com/Joshuaoneheart/Fintech-Final-Report

https://github.com/KaijuML/rotowire-rg-metric

https://github.com/Katsumata420/bert_mlm

https://github.com/KeithGalli/pycon2020