https://github.com/FengSuSky/CCB-VQA
Counterfactual Samples Synthesizing for Robust Visual Question Answering
Papers with Code 아카이브
Question Answering
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- 평가표
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- 보존된 평가 기록
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Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FerryHuang/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/FlamingHorizon/VRRM
https://github.com/FlorianPfisterer/2D-LSTM-Seq2Seq
https://github.com/Fuchai/language
https://github.com/FudanNLP/UNet
U-Net: Machine Reading Comprehension with Unanswerable Questions
https://github.com/FuxiaoLiu/LRV-Instruction
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
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/GauthierDmn/question_answering
https://github.com/GauthierDmn/question_generation
https://github.com/GeorgeLuImmortal/Hierarchical-BERT-Model-with-Limited-Labelled-Data
https://github.com/Georgetown-IR-Lab/curricula-neural-ir
https://github.com/Glaceon31/Document-Transformer
https://github.com/GlassyWing/transformer-keras
https://github.com/GoodAI/goodai-ltm-benchmark
https://github.com/GraphSAINT/GraphSAINT
https://github.com/GuessWhatGame/clevr
https://github.com/Gunnika/Visual-Question-Answering
https://github.com/Guowei-Yan/Data
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/HHousen/TransformerSum
https://github.com/HIT-SCIR/ELMoForManyLangs
https://github.com/HKUST-KnowComp/MnemonicReader
https://github.com/HKUST-KnowComp/R-Net
https://github.com/HSabbar/Question-Answering
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/HanhaiNotHai/transformer
https://github.com/Hanlard/Bert-for-WebQA
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
https://github.com/Hannibal046/nanoDPR
https://github.com/Harry-Muzart/harry-muzart.github.io
https://github.com/HazyResearch/hgcn
https://github.com/HeapHop30/graph-attention-nets
https://github.com/Heidelberg-NLP/discourse-aware-semantic-self-attention
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/Helsinki-NLP/OpenNMT-py
https://github.com/Helsinki-NLP/shared-info
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.