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/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhaskumarsinha/MinimalGPT
https://github.com/abhiagg92/bert_sentiment
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhilash1910/BERTSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhilashreddys/Fake-News-Article
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhineet/sentence_classification_pubmed_scibert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/abhishekanand1710/noiseandbias
https://github.com/abmitra84/Language_Modeling
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aerinkim/squad_2018
https://github.com/aethercortex/llama-x
https://github.com/af-ai-center/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/af-ai-center/nerblackbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ai21labs/lm-evaluation
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/airsplay/vimpac
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/ajenningsfrankston/NIPS2018_DECAPROP-master
Densely Connected Attention Propagation for Reading Comprehension
https://github.com/ajoshi80/QANet
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/akamath11/Multiple-choice-comprehension
https://github.com/akanyaani/miniLLAMA
https://github.com/aleksac99/SQuAD-SR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/algharak/BERTenhance
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alibaba-damo-academy/spokennlp
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
https://github.com/aliyun/qwen-dianjin
https://github.com/allenai/Break
https://github.com/allenai/allennlp-reading-comprehension
https://github.com/allenai/break-evaluator
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/macaw
https://github.com/allenyummy/EHR_NER
https://github.com/amauriciorr/AubreyBot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/qa-dataset-converter
https://github.com/ambityga/memsem
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.