Papers with Code 아카이브

Open-Domain Question Answering

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하위 태스크: 0
평가표: 15
보존된 평가 기록: 109

Open-domain question answering is the task of question answering on open-domain datasets such as Wikipedia.

코드

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

https://github.com/gchhablani/ReCAM

Gated-Attention Readers for Text Comprehension

https://github.com/ghus75/Question_Answering

Bidirectional Attention Flow for Machine Comprehension

https://github.com/google/active-qa

SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

https://github.com/gregtozzi/deep_learning_celnav

ktrain: A Low-Code Library for Augmented Machine Learning

https://github.com/hackerchenzhuo/LaKo

Distilling Knowledge from Reader to Retriever for Question Answering

https://github.com/han-shi/SparseBERT

Generating Long Sequences with Sparse Transformers

https://github.com/hihellohowareyou/RESREF_Chatbot_data_for_Korean

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/hitvoice/DrQA

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/hltcoe/colbert-x

Relevance-guided Supervision for OpenQA with ColBERT

https://github.com/hongyuntw/DPR

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

https://github.com/hongyuntw/DPR_BESS

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

https://github.com/huangxt39/BART_on_COVID_dialogue

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/i2r-simmc/i2r-simmc-2020

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/iCompass-ai/TunBERT

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/ielab/characterbert-dr

Dealing with Typos for BERT-based Passage Retrieval and Ranking

https://github.com/ivabojic/sleepqa

ELI5: Long Form Question Answering

https://github.com/jiachangliu/kategpt3

What Makes Good In-Context Examples for GPT-$3$?

https://github.com/jiacheng-xu/text-sum-uncertainty

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/jingtaozhan/DRhard

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/jingtaozhan/JPQ

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/jingtaozhan/LearningToRetrieve

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/john-bradshaw/rxn-lm

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/jonahwinninghoff/Text-Summarization

Generating Long Sequences with Sparse Transformers

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

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/junnyu/dpr_paddle

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/junnyu/paddle_reformer

Reformer: The Efficient Transformer

https://github.com/khushsi/Finetuning_BART_for_FACET_Summarization

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/kokikwbt/deepsect

Bidirectional Attention Flow for Machine Comprehension

https://github.com/kylrth/procedure-generation

Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering

https://github.com/libertatis/mrc-cbt

Text Understanding with the Attention Sum Reader Network

https://github.com/lmn-extracts/dcn_plus

Bidirectional Attention Flow for Machine Comprehension

https://github.com/lucashueda/long_sentence_transformer

Reformer: The Efficient Transformer

https://github.com/lucidrains/marge-pytorch

Distilling Knowledge from Reader to Retriever for Question Answering

https://github.com/lucidrains/mlm-pytorch

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/lucidrains/reformer-pytorch

Reformer: The Efficient Transformer

https://github.com/luyug/GC-DPR

Dense Passage Retrieval for Open-Domain Question Answering

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