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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/benjamintaiwo/Attention

https://github.com/bernard-yang/distilltablecot

https://github.com/bhavitvyamalik/bert

https://github.com/binhetech/bert-application

https://github.com/biyoml/End-to-End-Mandarin-ASR

https://github.com/biyoml/Pytorch-End-to-End-ASR-on-TIMIT

https://github.com/bjascob/amrlib

https://github.com/bkoch4142/attention-is-all-you-need-paper

https://github.com/bluejurand/Kaggle_QA_Google_Labeling

https://github.com/bplank/teaching-dl4nlp

https://github.com/brainsqueeze/text2vec

https://github.com/brandontrabucco/scaled_dot_product_attention

https://github.com/brightmart/bert_customized

https://github.com/brightmart/bert_original

https://github.com/bshantam97/Attention_Based_Networks

https://github.com/bytedance/neurst

https://github.com/bzhangGo/zero

https://github.com/cadia-lvl/fastspeech2

https://github.com/cahya-wirawan/indonesian-language-models

https://github.com/camlaedtke/NLP

https://github.com/caoyujiALgLM/NLP

https://github.com/caoyujiALgLM/NLP-progress

https://github.com/chalothon/BERT_Practice

https://github.com/chandu7077/mybert

https://github.com/chao-ji/tf-seq2seq

https://github.com/chen-xiong-yi/OwnBERT

https://github.com/chengchingwen/transformers.jl

https://github.com/chiayewken/bert-qa

https://github.com/chrisleunglokhin/Capstone-BERT

https://github.com/christianspybrook/article_recommender

https://github.com/cloudmcloudyo/capstone

https://github.com/cmsflash/efficient-attention

https://github.com/coco60/bert-test

https://github.com/colorfulscoop/tfdlg

https://github.com/conceptofmind/LaMDA-pytorch

https://github.com/conceptofmind/lamda-rlhf-pytorch