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Title: GitHub - VumBleBot/odqa_baseline_code: Baseline code for Korean open domain question answering(ODQA)

Open Graph Title: GitHub - VumBleBot/odqa_baseline_code: Baseline code for Korean open domain question answering(ODQA)

X Title: GitHub - VumBleBot/odqa_baseline_code: Baseline code for Korean open domain question answering(ODQA)

Description: Baseline code for Korean open domain question answering(ODQA) - VumBleBot/odqa_baseline_code

Open Graph Description: Baseline code for Korean open domain question answering(ODQA) - VumBleBot/odqa_baseline_code

X Description: Baseline code for Korean open domain question answering(ODQA) - VumBleBot/odqa_baseline_code

Opengraph URL: https://github.com/VumBleBot/odqa_baseline_code

X: @github

direct link

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KoNLPy ๊ณต์‹ ํ™ˆํŽ˜์ด์ง€https://konlpy.org/en/latest/install/
์ด์Šˆ ์ฐธ๊ณ https://github.com/VumBleBot/odqa_baseline_code/issues/82
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https://patch-diff.githubusercontent.com/VumBleBot/odqa_baseline_code#contributors
๊ตฌ๊ฑด๋ชจ(ggm1207)https://github.com/ggm1207
๊น€์„ฑ์ต(SeongIkKim)https://github.com/SeongIkKim
๊น€์ข…ํ—Œ(olenmg)https://github.com/olenmg
์‹ ์ง€์˜(ebbunnim)https://github.com/ebbunnim
์ด์ˆ˜์—ฐ(sooyounlee)https://github.com/sooyounlee
https://patch-diff.githubusercontent.com/VumBleBot/odqa_baseline_code#reference
https://patch-diff.githubusercontent.com/VumBleBot/odqa_baseline_code#papers
Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answeringhttps://arxiv.org/abs/2009.10791
Dense Passage Retrieval for Open-Domain Question Answeringhttps://arxiv.org/abs/2004.04906
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representationshttps://arxiv.org/abs/2102.10073
Improvements to BM25 and Language Models Examinedhttp://www.cs.otago.ac.nz/homepages/andrew/papers/2014-2.pdf
SpanBERT: Improving Pre-training by Representing and Predicting Spanshttps://arxiv.org/abs/1907.10529
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (SIGIR'20).https://arxiv.org/abs/2004.12832
Reading Wikipedia to Answer Open-Domain Questionshttps://arxiv.org/abs/1704.00051
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