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Question Generation
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The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/uds-lsv/unanswerable-question-generation
공식 코드A Lightweight Method to Generate Unanswerable Questions in English
https://github.com/umass-ml4ed/question-gen-aug-ranking
공식 코드Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
https://github.com/umass-ml4ed/socratic-quest-gen
공식 코드Improving Socratic Question Generation using Data Augmentation and Preference Optimization
https://github.com/unicamp-dl/corpus2question
공식 코드Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
https://github.com/vaibhav4595/ClarQ
공식 코드ClarQ: A large-scale and diverse dataset for Clarification Question Generation
https://github.com/valentinapy/clarifyd
공식 코드https://github.com/valentinapy/roleqgeneration
공식 코드Asking It All: Generating Contextualized Questions for any Semantic Role
https://github.com/victorywys/Learning2Ask_TypedDecoder
공식 코드Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
https://github.com/vishwajeet93/clqg
공식 코드https://github.com/vivisimo/QuestionGeneration
공식 코드https://github.com/wangsygit/stepwiseqa
공식 코드Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
https://github.com/webis-de/scolia25-xsum
공식 코드Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
https://github.com/weipingfu/qgeval
공식 코드QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation
https://github.com/whb139426/qa-prompts-eccv-24
공식 코드https://github.com/wjko2/INQUISITIVE
공식 코드Inquisitive Question Generation for High Level Text Comprehension
https://github.com/woonsangcho/contrast_qgen
공식 코드https://github.com/wowcz/longmit
공식 코드https://github.com/xinyadu/harvestingQA
공식 코드Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
https://github.com/xinyadu/nqg
공식 코드Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/yanghoonkim/neural_question_generation
공식 코드Improving Neural Question Generation using Answer Separation
https://github.com/yashkant/concat-vqa
공식 코드https://github.com/yonatanbitton/wysiwyr
공식 코드What You See is What You Read? Improving Text-Image Alignment Evaluation
https://github.com/yueeeeeeee/caqa
공식 코드Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
https://github.com/yzpang/gold-off-policy-text-gen-iclr21
공식 코드https://github.com/zeaver/multifactor
공식 코드Improving Question Generation with Multi-level Content Planning
https://github.com/zhaozj89/Educational-Question-Generation
공식 코드https://github.com/zide05/mtg
공식 코드https://github.com/zihao-ai/vdc
공식 코드https://github.com/zipengxuc/ADVSE-GuessWhat
공식 코드Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/AndreasInk/Quiz-APIv2
https://github.com/ArvinZhuang/BiTAG
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Billijk/DeepQuestionAsking
https://github.com/CLUEbenchmark/LGEB
Learning to Ask: Neural Question Generation for Reading Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.