https://github.com/Mind23-2/MindCode-131
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Stochastic Answer Networks for Machine Reading Comprehension
XLNet: Generalized Autoregressive Pretraining for Language Understanding
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Learning to Ask: Neural Question Generation for Reading Comprehension
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
mT5: A massively multilingual pre-trained text-to-text transformer
XLNet: Generalized Autoregressive Pretraining for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Attention-over-Attention Neural Networks for Reading Comprehension
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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.