https://github.com/INK-USC/KagNet
공식 코드KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
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/INK-USC/MHGRN
공식 코드Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
https://github.com/INK-USC/NExT
공식 코드https://github.com/INK-USC/deceive-KG-models
공식 코드Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
https://github.com/IVGSZ/Flash-VStream
공식 코드Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams
https://github.com/IemProg/QUAD
공식 코드No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only Memory
https://github.com/IllusoryVQA/IllusoryVQA
공식 코드Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions
https://github.com/IlyaGusev/summarus
공식 코드Advances of Transformer-Based Models for News Headline Generation
https://github.com/IntelLabs/academic-budget-bert
공식 코드https://github.com/IntelliSensing/UniRS
공식 코드UniRS: Unifying Multi-temporal Remote Sensing Tasks through Vision Language Models
https://github.com/InterDigitalInc/DialogSummary-VideoQA
공식 코드On the hidden treasure of dialog in video question answering
https://github.com/JXZe/DualVD
공식 코드DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
https://github.com/Jacob-Zhou/SynEval
공식 코드https://github.com/JasonForJoy/BRIEF
공식 코드BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
https://github.com/Javiery3889/AttackSeqBench
공식 코드https://github.com/Javihaus/Optimizing-AI-Reasoning
공식 코드Optimizing AI Reasoning: A Hamiltonian Dynamics Approach to Multi-Hop Question Answering
https://github.com/JeanKaddour/WASAM
공식 코드https://github.com/JerichoWorld/JerichoWorld
공식 코드https://github.com/JingruG/GATHER
공식 코드Open-Set Knowledge-Based Visual Question Answering with Inference Paths
https://github.com/Jirl-upenn/VLMnav
공식 코드https://github.com/JiwanChung/long-story-short
공식 코드Long Story Short: a Summarize-then-Search Method for Long Video Question Answering
https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb
공식 코드https://github.com/Jumperkables/tvqa_modality_bias
공식 코드https://github.com/Jun-jie-Huang/CoCLR
공식 코드CoSQA: 20,000+ Web Queries for Code Search and Question Answering
https://github.com/Junjue-Wang/EarthVQA
공식 코드https://github.com/JunweiLiang/FVTA_MemexQA
공식 코드https://github.com/KGQA/KGQA-datasets-generalization
공식 코드https://github.com/KNOT-FIT-BUT/JointSpanExtraction
공식 코드https://github.com/KNOT-FIT-BUT/R2-D2
공식 코드Pruning the Index Contents for Memory Efficient Open-Domain QA
https://github.com/KazutoshiShinoda/VQAG
공식 코드https://github.com/Koukyosyumei/secure_ml
공식 코드Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy
https://github.com/Kyubyong/sudoku
공식 코드https://github.com/L-ENA/HealthINF2020
공식 코드Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
https://github.com/LCS2-IIITD/DiffQue-TIST
공식 코드DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
https://github.com/LLaVA-VL/LLaVA-NeXT
공식 코드LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
https://github.com/LUMIA-Group/TosT5
공식 코드Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.