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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/bangliu/ACS-QG
공식 코드Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/bernardoleite/fairytaleqa-translated
공식 코드https://github.com/bernardoleite/few-shot-prompting-qg-control
공식 코드On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
https://github.com/bernardoleite/question-generation-control
공식 코드Towards Enriched Controllability for Educational Question Generation
https://github.com/bflashcp3f/synthetic-clinical-qa
공식 코드Give me Some Hard Questions: Synthetic Data Generation for Clinical QA
https://github.com/bio-nlp/medqg
공식 코드https://github.com/blender-nlp/entityconditionedqgen
공식 코드https://github.com/blmoistawinde/KPCNet
공식 코드Diverse and Specific Clarification Question Generation with Keywords
https://github.com/ckosten/spider4sparql
공식 코드Spider4SPARQL: A Complex Benchmark for Evaluating Knowledge Graph Question Answering Systems
https://github.com/clovaai/FocusSeq2Seq
공식 코드https://github.com/dataminr-ai/event-extraction-as-question-generation-and-answering
공식 코드https://github.com/dayihengliu/CRQDA
공식 코드Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
https://github.com/dengyang17/pacific
공식 코드https://github.com/dkalpakchi/quinductor
공식 코드https://github.com/dkalpakchi/swe_quinductor
공식 코드Automatically generating question-answer pairs for assessing basic reading comprehension in Swedish
https://github.com/dongxiangjue/closed-book-question-generation-via-contrastive-learning
공식 코드https://github.com/dpappas/Data-Augmentation-for-Biomedical-Factoid-Question-Answering
공식 코드https://github.com/dukesun99/CQG-MBQA
공식 코드A General Framework for Producing Interpretable Semantic Text Embeddings
https://github.com/dxlong2000/cohs-cqg
공식 코드CoHS-CQG: Context and History Selection for Conversational Question Generation
https://github.com/dxlong2000/sg-cqg
공식 코드Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
https://github.com/elehman16/discq
공식 코드https://github.com/esteng/ambiguous_vqa
공식 코드Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA
https://github.com/eth-lre/mwptutor
공식 코드AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails
https://github.com/eth-nlped/scaffolding-generation
공식 코드Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
https://github.com/factiverse/questgen
공식 코드QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications
https://github.com/flowersteam/eager
공식 코드EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL
https://github.com/fossr-project/ontogenia-cini
공식 코드Bench4KE: Benchmarking Automated Competency Question Generation
https://github.com/gipplab/physwikiquiz
공식 코드Collaborative and AI-aided Exam Question Generation using Wikidata in Education
https://github.com/google-research-datasets/NewsQuizQA
공식 코드https://github.com/google-research-datasets/maverics
공식 코드https://github.com/google-research/language
공식 코드A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
https://github.com/google-research/true
공식 코드https://github.com/gouqi666/rast
공식 코드Diversify Question Generation with Retrieval-Augmented Style Transfer
https://github.com/gsarti/it5
공식 코드IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/gtfintechlab/kg-qagen
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.