https://github.com/HKUST-KnowComp/MnemonicReader
Reinforced Mnemonic Reader for Machine Reading Comprehension
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Machine Reading Comprehension
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Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KLUE-benchmark/KLUE-baseline
https://github.com/Katherinaxxx/Paper-ReadingNotes
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
https://github.com/MindCode-4/code-11/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
https://github.com/MindCode-4/code-13/tree/main/stochastic-attention-head-removal-a-simple
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/chinesebert
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_reading_comprehension/DuReader-robust
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/machine_reading_comprehension/DuReader-yesno
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
https://github.com/PaddlePaddle/Research
https://github.com/PaddlePaddle/models
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
https://github.com/PetrosStav/BioMRC_code
BIOMRC: A Dataset for Biomedical Machine Reading Comprehension
https://github.com/RohanSaxena14/mrc-for-flat-nested-ner-in-tensoflow
https://github.com/RookieZB/duma_implementation_by_tf2
https://github.com/ShannonAI/Entity-Relation-As-Multi-Turn-QA
https://github.com/Xaniar87/SAN_SQuAD2
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/ZHEvent/ZHEvent.github.io
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
https://github.com/aerinkim/squad_2018
https://github.com/alibaba-damo-academy/spokennlp
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
https://github.com/allenyummy/EHR_NER
https://github.com/amenra/ranx
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/axiomlab/Cable
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/baidu/DuReader
https://github.com/brightmart/roberta_zh
https://github.com/cbluebenchmark/cblue
https://github.com/chenshaowei57/BMRC
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
https://github.com/chiahsuan156/ODSQA
https://github.com/code-implementation1/Code7/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/cooelf/SG-Net
https://github.com/csitfun/logiqa2.0
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
https://github.com/daltonj/treccastweb
TREC CAsT 2019: The Conversational Assistance Track Overview
https://github.com/dfcf93/MSMARCO
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/ewrfcas/Reinforced-Mnemonic-Reader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/facebookresearch/reconsider
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
https://github.com/fatyin88/MCQA_project
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
https://github.com/fulstock/mrc_nested_ner_ru
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.