Papers with Code 아카이브
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/xycforgithub/Airbnb-Comparison
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
https://github.com/xydaytoy/BMI-NMT
https://github.com/xzhang97666/alpacare
https://github.com/yahah100/text_summarization
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/yakazimir/semantic_fragments
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
https://github.com/yale-lily/ehrkit-2022
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
https://github.com/yanchunyu71/relational-networks-paddle
https://github.com/yandex-research/sparqling-queries
https://github.com/yangalan123/amortized-interpretability
Can Explanations Be Useful for Calibrating Black Box Models?
https://github.com/yangletliu/finlora
https://github.com/yangliuy/HybridNCM
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/yangliuy/NeuralResponseRanking
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/yangliuy/aNMM-CIKM16
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
https://github.com/yangrui123/Hidden
https://github.com/yangyi-chen/maya
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
https://github.com/yangyucheng000/Paper-3/tree/main/FBPrompt
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
https://github.com/yangyucheng000/University/tree/main/model-1/nystromformer
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/yangyucheng000/University/tree/main/model-1/qwen2
https://github.com/yangyucheng000/University/tree/main/model-1/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/University/tree/main/model-2/electra
ELECTRAMed: a new pre-trained language representation model for biomedical NLP
https://github.com/yangyucheng000/University/tree/main/model-2/ernie
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_moe
https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/yangyucheng000/University/tree/main/model-3/rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/yangyucheng000/University/tree/main/model-3/reformer
https://github.com/yangyucheng000/University/tree/main/model-3/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/yangyucheng000/University/tree/main/model-3/roberta
https://github.com/yangyucheng000/University/tree/main/model-3/splinter
https://github.com/yangyucheng000/University/tree/main/model-3/squeezebert
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/yangyucheng000/VOGUE
https://github.com/yangyucheng000/ascend_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_Ascend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_GPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/papercode-2/tree/main/LRSL-main
https://github.com/yangzonglin1994/bilm-tf-extended
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.