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/shmsw25/GraphRetriever

Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering

https://github.com/shmsw25/bart-closed-book-qa

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

https://github.com/shmsw25/qa-transfer

Bidirectional Attention Flow for Machine Comprehension

https://github.com/siqims/ARC-Challenge

Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering

https://github.com/skt-ai/kobart

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

https://github.com/sliao-mi-luku/Chatbot-Reformer

Reformer: The Efficient Transformer

https://github.com/sliao-mi-luku/NLP-Chatbot-Reformer-Trax

Reformer: The Efficient Transformer

https://github.com/snjstudent/MyREALM

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/stanford-futuredata/Baleen

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

https://github.com/stanfordnlp/dsp

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

https://github.com/surekhamedapati/NLPA_NEO

Bidirectional Attention Flow for Machine Comprehension

https://github.com/suryaiyer/squad-bidaf

Bidirectional Attention Flow for Machine Comprehension

https://github.com/tangg555/sabart

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

https://github.com/tanyuqian/aspect-based-summarization

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

https://github.com/techit-limtiyakul/bidirectional-attention-flow

Bidirectional Attention Flow for Machine Comprehension

https://github.com/texttron/tevatron

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/thakur-nandan/beir-colbert

Relevance-guided Supervision for OpenQA with ColBERT

https://github.com/thefonseca/factorsum

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

https://github.com/thunlp-mt/promptgating4mctg

Reducing Transformer Depth on Demand with Structured Dropout

https://github.com/thunlp/OpenQA

https://github.com/thunlp/clueanchor

Atlas: Few-shot Learning with Retrieval Augmented Language Models

https://github.com/tikyau/bidaf-quick-test

Bidirectional Attention Flow for Machine Comprehension

https://github.com/timrozday/spl-indications-bart

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

https://github.com/tomerwolgithub/Break

Break It Down: A Question Understanding Benchmark

https://github.com/udnet96/BART-various-finetune

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

https://github.com/umutguneri/Question-Answering-Assistant

Deep Learning for Answer Sentence Selection

https://github.com/vanzytay/NIPS2018_DECAPROP

Densely Connected Attention Propagation for Reading Comprehension

https://github.com/vgaraujov/seq2seq-spanish-plms

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

https://github.com/vinayak19th/Brevis-2.0

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

https://github.com/wasimusu/MachineRC

Dynamic Coattention Networks For Question Answering

https://github.com/white127/squad-2.0-bidaf

Bidirectional Attention Flow for Machine Comprehension

https://github.com/wilson1yan/VideoGPT

Generating Long Sequences with Sparse Transformers

https://github.com/wooseok-AI/Korean_e2e_CR_BERT

SpanBERT: Improving Pre-training by Representing and Predicting Spans

https://github.com/wyu97/Easy-use-BART

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

https://github.com/xiangyue9607/CliniRC

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/xiaobaicxy/Bidaf_SQuAD_MC_Pytorch

Bidirectional Attention Flow for Machine Comprehension

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