Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 153
- 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/hithisisdhara/doc2vec
https://github.com/hitvoice/DrQA
https://github.com/hiun/learning-transformers
https://github.com/hiyouga/llama-efficient-tuning
https://github.com/hizvi/memN2N
https://github.com/hkuds/easyrec
https://github.com/hkunlp/batch-prompting
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
https://github.com/hkunlp/symgen
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
https://github.com/hkust-knowcomp/efo-1-qa-benchmark
https://github.com/hkust-knowcomp/knowledge-constrained-decoding
Towards a Unified Multi-Dimensional Evaluator for Text Generation
https://github.com/hkust-nlp/agentboard
https://github.com/hkust-nlp/b-star
Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations
https://github.com/hld67890/prml2020_pj
https://github.com/hltcoe/colbert-x
https://github.com/hmdolatabadi/acs
Coresets for Data-efficient Training of Machine Learning Models
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
https://github.com/hoichunlaw/COVID-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/hojjat-mokhtarabadi/promptsource
https://github.com/hongyuntw/DPR
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/hongyuntw/DPR_BESS
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/horizonheart/ELMO
https://github.com/hqhbench/hqhbench
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
https://github.com/hrbigelow/transformer-aiayn
https://github.com/hsakas/Recurrent-Entity-Network-EntNet
https://github.com/ht1221/leap-lstm
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
https://github.com/https-github-com-GiangHoang9912/ernie-gen
https://github.com/https-seyhan/BugAI
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/huang-research-group/InstructionGPT-4
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://github.com/huanghonggit/Mask-Language-Model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/huangxt39/BART_on_COVID_dialogue
https://github.com/huberemanuel/DeBERTa
https://github.com/hubertjb/dl-eeg-review
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/hubiodatalab/druggen
https://github.com/huchen365/ds
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.