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Answer Generation

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/huggingface/transformers

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/ibm/graph_ensemble_learning

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/itzprashu1/prashant

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/jongwooko/nash-pruning-official

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/junnyu/paddle_t5

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/lesterpjy/numeric-t5

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/luomancs/retriever_reader_for_okvqa

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/polyusmart/poll-question-generation

https://github.com/primeqa/primeqa

MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding

https://github.com/princeton-nlp/helmet

QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs

https://github.com/qipengguo/p2_webnlg2020

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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/salesforceairesearch/answer-engine-eval

Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage

https://github.com/samsam3232/qampari

QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs

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/translit-former

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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/uci-soe/fairytaleqadata

It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books

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/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/xiuyu0000/new_papers_codes/tree/main/t5

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/xiuyu0000/papers_with_examples/tree/main/vogue

VOGUE: Answer Verbalization through Multi-Task Learning

https://github.com/xuetianci/pacit

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/yangyucheng000/VOGUE

VOGUE: Answer Verbalization through Multi-Task Learning

https://github.com/yangyucheng000/papercode-2/tree/main/LRSL-main

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering

https://github.com/yizhongw/tk-instruct

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/yuyangdu01/llm-cl-vqa

Surgical-VQLA: Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery

https://github.com/zhiqic/chartreader

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.