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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/MichielDeRuiter/ConversationalClassification
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/Microsoft/AzureML-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Milan-Chicago/GLG-Automated-Meta-data-Tagging
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/Mind23-2/MindCode-100
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/Mind23-2/MindCode-131
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Mind23-2/MindCode-132
https://github.com/Mind23-2/MindCode-133
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-135
https://github.com/Mind23-2/MindCode-138
https://github.com/Mind23-2/MindCode-139
https://github.com/Mind23-2/MindCode-140
https://github.com/Mind23-2/MindCode-144
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-156
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/Mind23-2/MindCode-160
https://github.com/Mind23-2/MindCode-161
https://github.com/Mind23-2/MindCode-166
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/Mind23-2/MindCode-68
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/Mind23-2/MindCode-98
https://github.com/Mind23-2/MindCode-99
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindCode-4/code-1/tree/main/tvlt
https://github.com/MindCode-4/code-1/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-10/tree/main/FDDE/train-fine
https://github.com/MindCode-4/code-11/tree/main/a-self-attentive-model-for-knowledge-tracing
https://github.com/MindCode-4/code-11/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
https://github.com/MindCode-4/code-11/tree/main/factorized-attention
https://github.com/MindCode-4/code-12/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-12/tree/main/gaussian_adaptive_attention
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-12/tree/main/model-conversion-via
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/MindCode-4/code-13/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindCode-4/code-13/tree/main/stochastic-attention-head-removal-a-simple
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-3/tree/main/deberta
https://github.com/MindCode-4/code-3/tree/main/deberta_v2
https://github.com/MindCode-4/code-3/tree/main/distilbert
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.