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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/allenai/allennlp-reading-comprehension

Bidirectional Attention Flow for Machine Comprehension

https://github.com/allenai/break-evaluator

Break It Down: A Question Understanding Benchmark

https://github.com/allenai/scientific-claim-generation

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

https://github.com/amore-upf/masked-coreference

SpanBERT: Improving Pre-training by Representing and Predicting Spans

https://github.com/amzn/seqzero

Autoregressive Entity Retrieval

https://github.com/andreiilie1/dynamic_coattention_networks

Dynamic Coattention Networks For Question Answering

https://github.com/asahi417/lm-question-generation

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

https://github.com/awalther/scibart

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

https://github.com/baichuan-inc/baichuan2

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

https://github.com/baidu/DuReader

Bidirectional Attention Flow for Machine Comprehension

https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets

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

https://github.com/bfopengradient/NLP_text_summarization_apps

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

https://github.com/bloomsburyai/question-generation

Multi-Perspective Context Matching for Machine Comprehension

https://github.com/brmson/dataset-sts

Deep Learning for Answer Sentence Selection

https://github.com/c00k1ez/plain-transformers

Reducing Transformer Depth on Demand with Structured Dropout

https://github.com/carriex/lfqa_eval

Hurdles to Progress in Long-form Question Answering

https://github.com/chakravarthi-v/Polaroid-1

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

https://github.com/codelion/optillm/blob/main/optillm/entropy_decoding.py

Entropy-Based Decoding for Retrieval-Augmented Large Language Models

https://github.com/cosmoquester/2021-dialogue-summary-competition

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

https://github.com/cosmoquester/transformers-bart-pretrain

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

https://github.com/davidgolub/QuestionGeneration

Bidirectional Attention Flow for Machine Comprehension

https://github.com/dawn0815/UniSA

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

https://github.com/deepset-ai/haystack

REALM: Retrieval-Augmented Language Model Pre-Training

https://github.com/denisewong1/ASX300

Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs

https://github.com/distractor-generation/dg_survey

Bidirectional Attention Flow for Machine Comprehension

https://github.com/dmis-lab/covidAsk

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

https://github.com/dmis-lab/covidsearch

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

https://github.com/dsouzadaniel/BiDAF

Bidirectional Attention Flow for Machine Comprehension

https://github.com/efficientqa/retrieval-based-baselines

Dense Passage Retrieval for Open-Domain Question Answering

https://github.com/epfl-dlab/multilingual-entity-insertion

EntQA: Entity Linking as Question Answering

https://github.com/facebookresearch/ParlAI

Reading Wikipedia to Answer Open-Domain Questions

https://github.com/facebookresearch/bart_ls

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

https://github.com/facebookresearch/editeval

KILT: a Benchmark for Knowledge Intensive Language Tasks

https://github.com/facebookresearch/reconsider

RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering

https://github.com/fwbrandao/Abstractive_Summarisation

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

https://github.com/galsang/BiDAF-pytorch

Bidirectional Attention Flow for Machine Comprehension

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