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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/umutguneri/Question-Answering-Assistant
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/usyd-fsalab/fp6_llm
https://github.com/utterworks/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uva-dsa/ems-pipeline
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uwnlp/geosolver
https://github.com/uygarkurt/BERT-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/v0lta/Complex-gated-recurrent-neural-networks
https://github.com/vailatuts/SSFG-regularization
https://github.com/valdecy/pybibx
https://github.com/valentinmace/noisy-text
https://github.com/vanboefer/nn_doc2vec_exercise
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/vanzytay/NIPS2018_DECAPROP
Densely Connected Attention Propagation for Reading Comprehension
https://github.com/vanzytay/WSDM2018_HyperQA
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
https://github.com/varepsilon/clef2019-prosody
Prosody Modifications for Question-Answering in Voice-Only Settings
https://github.com/vargpt-family/vargpt-v1.1
https://github.com/varshakr24/CommonsenseReasoning
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
https://github.com/varun-suresh/experiments-with-gpt2
https://github.com/varunchaudharycs/aNswER_multirc
Repurposing Entailment for Multi-Hop Question Answering Tasks
https://github.com/varunchaudharycs/multi_rc
Repurposing Entailment for Multi-Hop Question Answering Tasks
https://github.com/vchopin/ERNIE_Emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/vchudinov/dynamic_memory_networks_with_keras
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/vcskaushik/LLMzip
https://github.com/vectara/hallucination-leaderboard
AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
https://github.com/vedantgaur/symbolic-mwp-reasoning
Are NLP Models really able to Solve Simple Math Word Problems?
https://github.com/veekaybee/what_are_embeddings
https://github.com/venkyr91193/Sentiment-Analysis-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/venkyr91193/Sentiment_Analysis_with_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vgaraujov/lll-nlp
https://github.com/vicentvankor/sun-shine
https://github.com/viethoang1512/kpa
https://github.com/vijay120/bert
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.