Papers with Code 아카이브
Open-Domain Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FenQQQ/Fusion-in-decoder
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/GauthierDmn/question_answering
https://github.com/HHousen/TransformerSum
https://github.com/HKUST-KnowComp/MnemonicReader
https://github.com/Hannibal046/nanoDPR
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/IBM/ColBERT-practical
https://github.com/KNOT-FIT-BUT/scalingQA
Pruning the Index Contents for Memory Efficient Open-Domain QA
https://github.com/KushGrandhi/Polaroid
https://github.com/Lou1sM/AML-Project
https://github.com/Lou1sM/AdvancedML-Project-Dynamic-Coattention-Networks
https://github.com/Mind23-2/MindCode-132
https://github.com/Mind23-2/MindCode-160
https://github.com/MindCode-4/code-11/tree/main/factorized-attention
https://github.com/MindCode-4/code-3/tree/main/efficientformer
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0
ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/Raman-Raje/Machine-Reading-Comprehension-Neural-Question-Answer-
https://github.com/RicardoL1u/GraftNet-Unofficial
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
https://github.com/Rick-McCoy/Reformer-pytorch
https://github.com/Shravsridhar/NLP-Question-Answer-Model
https://github.com/Shravsridhar/QA-Model-INFO-7374
https://github.com/UnknownGenie/altered-BERT-KPE
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/Vakihito/SentimentYoutube
https://github.com/W4ngatang/qags
https://github.com/Wadaboa/squad-question-answering
https://github.com/WarruzuEndo/BiDAF_mindspore
https://github.com/ZHO9504/Select-Key-Terms-in-a-Question
https://github.com/ZIZUN/MAFiD
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
https://github.com/Zefty/rag-end2end-retriever
https://github.com/aaronbae/AnaQA
https://github.com/aartika/experiment1
https://github.com/ajenningsfrankston/NIPS2018_DECAPROP-master
Densely Connected Attention Propagation for Reading Comprehension
https://github.com/alexlimh/DPR_MUF
https://github.com/alibaba-nlp/webagent
https://github.com/allenai/Break
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.