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/Rami97rgb/French-to-English-Translator
https://github.com/Remiphilius/PoemesProfonds
https://github.com/RenXiangyuan/tf_bert
https://github.com/RenzKa/simlingo
https://github.com/RicardoL1u/GraftNet-Unofficial
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
https://github.com/Rick-McCoy/Reformer-pytorch
https://github.com/Rishit-dagli/Nystromformer
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/RitAreaSciencePark/ZigZagLLMs
https://github.com/RobertKirk/roam-tools
https://github.com/RonRaifer/BERT-Ghazali
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Rudedaisy/attention-is-all-you-need-pytorch
https://github.com/RundongChou/elmo-chinese-oversimplified
https://github.com/RussianNLP/MOROCCO
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
https://github.com/RxstydnR/Stock_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/RylanSchaeffer/prismatic-vlms
Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/SAP-samples/acl2019-commonsense
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SAP-samples/acl2019-commonsense-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SCismycat/bert_code_view
https://github.com/SDLZY/VCR_Align
https://github.com/SGNovice/Disease-detection-using-chest-xrays
https://github.com/SJHBXShub/Question_pair
Convolutional Neural Network Architectures for Matching Natural Language Sentences
https://github.com/SYangDong/bert-with-frozen-code
https://github.com/SambhawDrag/XLNet.jl
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/SamuelCahyawijaya/indobenchmark-toolkit
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation
https://github.com/Samyu0304/thought-propagation
https://github.com/Sanyuan-Chen/RecAdam
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SasCezar/XWikiRE
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
https://github.com/Satan012/BERT
https://github.com/SatyamSoni23/Smart-Question-Answering-System-on-Document
Adversarial Examples for Evaluating Reading Comprehension Systems
https://github.com/Saurabh2798/Q-A-bot
https://github.com/ScandEval/ScandEval
ScandEval: A Benchmark for Scandinavian Natural Language Processing
https://github.com/SeanCherngTW/NTCIR-STC3-NDDQsubtask
Short Text Conversation Based on Deep Neural Network and Analysis on Evaluation Measures
https://github.com/SelinaFelton/SMA
https://github.com/SeonbeomKim/TensorFlow-MemN2N
https://github.com/SeoroMin/transformer_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.