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/jageshmaharjan/BERT_Service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jangjoongkeon/JK
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jd-ai-research-silicon-valley/sae
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
https://github.com/jeongukjae/KR-BERT-SimCSE
https://github.com/jiho-yesnlp/qytl
https://github.com/jinmang2/RetroReader
https://github.com/jinzhenfan/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jojonki/MemoryNetworks
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/jonahwinninghoff/Text-Summarization
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/joongbo/tta
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jopokemine/Dissertation
https://github.com/joshuaWang-bit/Textclassification-pytorch
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/joytianya/google_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jpablou/Matching-The-Blanks-Ths
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/juletx/lm-evaluation-harness
https://github.com/jungomi/swiss-language-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_finetune
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_transfer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junnyu/paddle_t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/kaituoxu/Listen-Attend-Spell
https://github.com/kamalkraj/ALBERT-TF2.0
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/kamalkraj/BERT-SQuAD
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kamalkraj/BERT_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/karenacorn99/explore-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/karpathy/build-nanogpt
https://github.com/karpathy/llama2.c
https://github.com/karpathy/llm.c
https://github.com/karta282950/bert-multiclass
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kayvr/token-hawk
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.