Reading Comprehension
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Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.
Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.
Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.
The Machine Reading group at UCL also provides an overview of reading comprehension tasks.
Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/keiyamashita111/aaproject
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kelly2016/multi-label-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kevinkwl/AoAReader
Attention-over-Attention Neural Networks for Reading Comprehension
https://github.com/keyonvafa/sequential-rationales
The LAMBADA dataset: Word prediction requiring a broad discourse context
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kiko441500/google_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcheng2000/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcong/gpu_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingoflolz/mesh-transformer-jax
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/kinimod23/NMT_Project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kirubarajan/roft
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/kirubarajan/trick
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/knuddj1/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/knuddy/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kobowon/cs470_project_version2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kokikwbt/deepsect
https://github.com/kpe/bert-for-tf2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kpot/keras-transformer
https://github.com/krafton-ai/korani
https://github.com/krantirk/BERT-Pretrained-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kriesbeck/masked-language-prediction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kyegomez/GPT3
https://github.com/kyubyong/bert_ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kyzhouhzau/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/labmlai/annotated_deep_learning_paper_implementations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lambert-x/prolab
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/lcs2-iiitd/emotion-flip-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lduml/blog
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/lehoanganh298/BERT-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lennonzurich/lalala
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/leozhoujf/DataSciComp
https://github.com/libertatis/mrc-cbt
https://github.com/linlei1214/SITS-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.