Papers with Code 아카이브
Question Answering
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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/AtmaHou/MetaDialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Aunsiels/demo_quasimodo
Commonsense Properties from Query Logs and Question Answering Forums
https://github.com/AutomatedProgramRepair-2021/dear-auto-fix
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Aveek-Saha/Graph-Attention-Net
https://github.com/Aveek-Saha/Transformer
https://github.com/Azure/AzureChestXRay
https://github.com/BAJUKA/SQuAD-NLP
https://github.com/BIG-S2/keras-gnm
https://github.com/BKAUTO/Trinocular-habitat-sim
https://github.com/BM-K/Question-Difficulty-Estimation
DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
https://github.com/BNN-UPC/MARL-GNN-TE
Is Machine Learning Ready for Traffic Engineering Optimization?
https://github.com/BPYap/BERTSel
https://github.com/BPYap/Question-Generation
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/gMLP
https://github.com/BangLiu/QANet-PyTorch
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/BasileBron/Artificial-Intelligence-Lexical
https://github.com/Beacontownfc/MegatronBert_paddle
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/Beacontownfc/paddle_luke_stable
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/Beacontownfc/rembert_paddle
Rethinking embedding coupling in pre-trained language models
https://github.com/Belval/NRTR
https://github.com/Bhavnicksm/vanilla-transformer-jax
https://github.com/BinWang28/BERT_Sentence_Embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BinWang28/SBERT-WK-Sentence-Embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/BinaLab/FloodNet-Challenge-EARTHVISION2021
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/BioIntelligence-Lab/openjphpy
https://github.com/BirgerMoell/bertcastle
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Bodoral/MuSiQue_replication
MuSiQue: Multihop Questions via Single-hop Question Composition
https://github.com/Bradley-Butcher/Conformers
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/BroCoLySTyLe/SQLovaReview
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Burf/tfdetection
https://github.com/CGCL-codes/naturalcc
https://github.com/CKRC24/Listen-and-Translate
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/CLUEbenchmark/CLUE
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/CLUEbenchmark/LGEB
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.