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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/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/robotics-4-all/ISSEL-Announcements
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/rohan400/PepperQA
https://github.com/rohanshad/cmr_transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rohit129/Movie_KnowledgeGraph_QA
https://github.com/rojagtap/abstractive_summarizer
https://github.com/ronentk/dbca-splitter
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
https://github.com/ronghanghu/snmn
Explainable Neural Computation via Stack Neural Module Networks
https://github.com/roshaan7zafar/-MAX-Text-Sentiment-Classifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/rosinality/relation-networks-pytorch
https://github.com/rowanz/r2c
https://github.com/rpuiggari/bert2
https://github.com/rubenpt91/pfl-docvqa-competition
https://github.com/ruc-aimc-lab/nt2vr
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/ruc-nlpir/flashrag
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
https://github.com/rucaibox/safe
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
https://github.com/ruckbreasoning/numkbqa
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
https://github.com/ruotianluo/iep-ref
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/rupakdas18/Research-Project-UMD-CS-5642
https://github.com/rupakdas18/SemEval-2017-Task-4-A-B-C-using-BERT
https://github.com/rupakdas18/SuperGlue-tasks-using-BERT
https://github.com/rutajoshi/cs231n-project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/rvlopes/gloria
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/rvstraalen/doc2vec-workshop
https://github.com/rwbfd/OpenCompetitionV2
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/rwightman/pytorch-image-models
https://github.com/rybread1/stack-overflow-question-classification
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/rzhangpku/MFAE
https://github.com/s-gupta/map-plan-baseline
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/s9891326/Fine-Tuning-BERT
https://github.com/saccharomycetes/text-based-traffic-understanding
https://github.com/sachinbiradar9/question-answer-selection
Applying Deep Learning to Answer Selection: A Study and An Open Task
https://github.com/sachit-n/drqa_doc_reader
https://github.com/sachith-gunasekara/t4d
How FaR Are Large Language Models From Agents with Theory-of-Mind?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.