Papers with Code 아카이브
Question Generation
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The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fuqiang-git-hub/unilmv1-Paddle
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/guihuzhang/FactSpotter
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/hadifar/question-generation
EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain
https://github.com/hlt-mt/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/hltchkust/cantonese-asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/https-github-com-GiangHoang9912/ernie-gen
https://github.com/huggingface/transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/hugochan/Graph2Seq-for-KGQG
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
https://github.com/iambabao/copynet
https://github.com/ibm/graph_ensemble_learning
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/imatge-upc/vqa-2016-cvprw
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jerinphilip/fairseq-ilmt
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/jiaruncao/BioCopyMechanism
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jungokasai/t2r
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lesterpjy/numeric-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/liamdugan/question_generation
https://github.com/libeineu/GPKD
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/libeineu/SDT-Training
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/lucylow/En_francais_si_vous_plait-
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/luomancs/retriever_reader_for_okvqa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/lurosenb/sass
https://github.com/lyh6560new/p3sum
https://github.com/marccanby/bidi_decoding
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/mgaido91/FBK-fairseq-ST
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/microsoft/ar2
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/microsoft/bang
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
https://github.com/microsoft/dialoglm
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.