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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/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder

https://github.com/ethan5437/PR-VIST

https://github.com/ethancaballero/neural-engineers-first-attempt

https://github.com/eunseo1092/Graduation_project

https://github.com/ewdowiak/Sicilian_Translator

https://github.com/exa-labs/exa-mcp-server

https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0

https://github.com/facebookresearch/fairseq

https://github.com/facebookresearch/xformers

https://github.com/faizansuhail89/bert

https://github.com/fanchenyou/transformer-study

https://github.com/fancyerii/bert

https://github.com/fciannel/bert_fciannel

https://github.com/feizc/Machine-Translation-Pytorch

https://github.com/felixSchober/ABSA-Transformer

https://github.com/fjiangai/mmapis

https://github.com/flxst/nerblackbox

https://github.com/flyliu2017/bert

https://github.com/flyliu2017/bert_modularized

https://github.com/fqyshelly/CS224n-Winter2019-assignment-solutions

https://github.com/fra-mari/The-Karl-Marx-Press-Review

https://github.com/frankcgq105/BERTCHEN

https://github.com/franknb/Self-attention-DCGAN

https://github.com/franknb/Text-Summarization

https://github.com/fstahlberg/tensor2tensor

https://github.com/gavincaoyuji/NLP

https://github.com/gggggeun/DeepLearning_Architecture

https://github.com/giagia96/DeepComedy

https://github.com/glouppe/info8010-deep-learning

https://github.com/gmum/dl-mo-2021

https://github.com/going-doer/paper2code

https://github.com/goldenbili/Bert_Test2

https://github.com/goldenbili/Bert_Test3

https://github.com/goldenbili/bert_lamb_pretrain

https://github.com/goodluck4s/bert-

https://github.com/google/flax/tree/master/examples/wmt