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Question Answering

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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/WebQnA/WebQA_Baseline

Unified Vision-Language Pre-Training for Image Captioning and VQA

https://github.com/WenYanger/General-Transformer-Pytorch

Attention Is All You Need

https://github.com/WenjieDu/PyPOTS

Attention Is All You Need

https://github.com/Wenlin-Chen/ADKF-IFT

Strategies for Pre-training Graph Neural Networks

https://github.com/WinnieHAN/dnhmm

Adding Gradient Noise Improves Learning for Very Deep Networks

https://github.com/Wolfie8935/Implementation-of-Attention-is-all-you-need

Attention Is All You Need

https://github.com/XINZXZHANG/Bert_repo

Attention Is All You Need

https://github.com/XUJitao/WordEmbedding

Attention Is All You Need

https://github.com/Xaniar87/SAN_SQuAD2

Stochastic Answer Networks for Machine Reading Comprehension

https://github.com/XapaJIaMnu/transformative

Attention Is All You Need

https://github.com/XingruiWang/superclevr-3D-question

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

https://github.com/Xinsen-Zhang/transformer

Attention Is All You Need

https://github.com/XuhanLiu/Transformer

Attention Is All You Need

https://github.com/Y1ran/NLP-BERT--ChineseVersion

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/YC-wind/embedding_study

Deep contextualized word representations

https://github.com/YChen1993/ELECRec

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/YIKUAN8/Clinical-Longformer

emrQA: A Large Corpus for Question Answering on Electronic Medical Records

https://github.com/YJHMITWEB/GLoMo-tensorflow

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

https://github.com/YYGXjpg/BERT_WL

Attention Is All You Need

https://github.com/Yindong-Zhang/myGAT

Graph Attention Networks

https://github.com/YinpeiDai/NAUM

Distributed Representations of Sentences and Documents

https://github.com/Yipeng91/text_classifier_pub

Attention Is All You Need

https://github.com/YongWookHa/BERT-on-Pytorch-Template

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/YongfeiYan/Neural-Document-Modeling

Neural Variational Inference for Text Processing

https://github.com/YuanEric88/bert-nlu

Attention Is All You Need

https://github.com/YuehChuan/bert-Agus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/YunseobShin/wiki_GAT

Graph Attention Networks

https://github.com/YunwenTechnology/Unilm

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/ZHEvent/ZHEvent.github.io

Interactive Grounded Language Acquisition and Generalization in a 2D World

https://github.com/ZHO9504/Select-Key-Terms-in-a-Question

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

https://github.com/ZHZisZZ/emulated-disalignment

Tuning Language Models by Proxy

https://github.com/ZIZUN/MAFiD

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

https://github.com/Zeeshan75/Bert_Telugu_Ner

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Zefty/rag-end2end-retriever

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/Zehui127/SQUAD_BERT

Attention Is All You Need

https://github.com/ZmeiGorynych/transformer_pytorch

Attention Is All You Need

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.