Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
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/ankurtaly/Integrated-Gradients
https://github.com/annaorosz/my_qanet_implementation
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/anshulwadhawan/ABSA
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/antoine77340/howto100m
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/antoinecollas/transformer_neural_machine_translation
https://github.com/anton-bushuiev/ppiformer
https://github.com/antonyvigouret/Pay-Attention-to-MLPs
https://github.com/anupamsingh610/bert_ner_stride
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/anuragreddygv323/Important-stuff
Natural Language Processing: State of The Art, Current Trends and Challenges
https://github.com/anuscode/nrms
https://github.com/aozhongzhang/magr
https://github.com/ap229997/Conditional-Batch-Norm
https://github.com/appcoreopc/berty
https://github.com/appl-team/appl
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
https://github.com/apugoneappu/ask_me_anything
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/apugoneappu/vqa_visualise
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/arangoml/fastgraphml
https://github.com/arcee-ai/PruneMe
https://github.com/ardakdemir/my_bert_ner
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/ari9dam/ILPME
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
https://github.com/arielsho/decomposition-table-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/arjunakula/neurips2021
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/arnoweng/CheXNet
https://github.com/arshadshk/SAKT-pytorch
https://github.com/artitw/BERT_QA
https://github.com/arvind385801/paraphrasegen
https://github.com/asadovsky/nn
Learning Recurrent Span Representations for Extractive Question Answering
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/relbert
https://github.com/asap7772/fewshot-preference-optimization
https://github.com/asfathermou/human-computer-interaction
https://github.com/asgaardlab/test-case-similarity-technique
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/asjad99/data_products
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
https://github.com/askaydevs/distillbert-qa
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.