Papers with Code 아카이브
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/moh833/VQA
https://github.com/neineis/multi-head-attention
https://github.com/obss/disgem
DisGeM: Distractor Generation for Multiple Choice Questions with Span Masking
https://github.com/orhonovich/q-squared
https://github.com/overwindows/PALM
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation
https://github.com/patil-suraj/question_generation
https://github.com/polyusmart/poll-question-generation
https://github.com/princeton-nlp/SimCSE
https://github.com/pwc-1/Paper-9/tree/main/1/xlm_prophetnet
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/rahmanidashti/acqsurvey
https://github.com/raymondhs/fairseq-extensible-mbart
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/robinsongh381/unilm_pytorch_korean
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/s-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/safakkbilici/Academic-Paper-Title-Recommendation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/sarapapi/fbk-fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/souvikshanku/digit-captioning
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/souvikshanku/translit-former
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/stevezheng23/fairseq_extension
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thu-keg/omnievent
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/thudm/swissarmytransformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/trisongz/question-answering-lightning
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/uabinf/nlp-fall-2019-project-anuradha_shinjitha
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/ukplab/m-amr2text
Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/um-arm-lab/efficient-eng-2-ltl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/utanaka2000/fairseq
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/vectara/hallucination-leaderboard
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/volcengine/vegiantmodel
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/wangcongcong123/ttt
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/windweller/PragmaticVQA
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.