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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/giagia96/DeepComedy
https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/gicheonkang/gst-visdial
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/gitlimlab/Relation-Network-Tensorflow
https://github.com/giuseppefutia/link-prediction-code
https://github.com/givenbysun/hyper-class-augmented-VQA
https://github.com/gkassel/pyaixi
https://github.com/glassroom/heinsen_routing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/glb400/Toy-RecLM
https://github.com/glee4810/EHRSQL
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmtt/CSCI590
https://github.com/gmum/dl-mo-2021
https://github.com/gmum/umwpl2021
https://github.com/gngdb/relational
https://github.com/gnn4dr/DRKG
https://github.com/going-doer/paper2code
https://github.com/goldenbili/Bert_Test2
https://github.com/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/goodluck4s/bert-
https://github.com/google-deepmind/narrativeqa
https://github.com/google-deepmind/proactive_t2i_agents
Evaluating Text-to-Visual Generation with Image-to-Text Generation
https://github.com/google-deepmind/rc-data
https://github.com/google-deepmind/streamingqa
StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models
https://github.com/google-deepmind/tracr
https://github.com/google-deepmind/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research-datasets/boolean-questions
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
https://github.com/google-research-datasets/wiki-reading
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
https://github.com/google-research/chain-of-table
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/active-qa
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/google/flax/tree/master/examples/wmt
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.