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/scut-bds/example_repo_from_scutbds
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sdadas/polish-roberta
https://github.com/seasonyao/Zero-Shot-Entity-Linking
https://github.com/sebastianarnold/TeXoo
SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
https://github.com/semal/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shaikhzhas/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shailaja183/vlqa
https://github.com/sharejing/BiPaR
https://github.com/sherinaseri/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shikhar1sharma/NLP-Resources
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/shimisalant/RaSoR
Learning Recurrent Span Representations for Extractive Question Answering
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shmsw25/GraphRetriever
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/shmsw25/qa-transfer
https://github.com/shreyashankar/gpt3-sandbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/shuhewang1998/gpt-ner
https://github.com/sidney1505/arc_maml_transformer
https://github.com/simon-benigeri/narrative-generation
https://github.com/siqims/ARC-Challenge
https://github.com/siramatu/brigade-visualizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/skoltech-nlp/rudetoxifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/smarton-empower/smarton-ai
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/smile-data/smile
https://github.com/socc-io/piqaboo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/somiltg/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/songmzhang/dskd
https://github.com/sooftware/End-to-End-Speech-Recognition-Models
https://github.com/sooftware/End-to-end-Speech-Recognition
https://github.com/sooftware/Fairseq-Listen-Attend-Spell
https://github.com/sooftware/OpenSpeech
https://github.com/soutsios/pos-tagger-bert-tensorflow
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/souvikshanku/translit-former
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/squeezeailab/squeezellm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.