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/g-eklund/bert_tutorial
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/galsang/BiDAF-pytorch
https://github.com/ganeshjawahar/interpret_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ganjinzero/rrhf
https://github.com/gargimahale/Speech-Recognition-Using-Tensorflow
https://github.com/gbcolborne/lang_id
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gcunhase/embracebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/geondopark/ckd
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/geraltofrivia/match-lstm-ptr-network
https://github.com/ggerganov/llama.cpp
https://github.com/gggggeun/DeepLearning_Architecture
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ggml-org/llama.cpp
https://github.com/ghus75/Question_Answering
https://github.com/glamor-usc/climb
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/glassroom/heinsen_routing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gmum/dl-mo-2021
https://github.com/goldenbili/Bert_Test2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/goldenbili/Bert_Test3
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/goldenbili/bert_lamb_pretrain
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/goodluck4s/bert-
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-deepmind/narrativeqa
https://github.com/google-deepmind/rc-data
https://github.com/google-deepmind/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research-datasets/boolean-questions
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
https://github.com/google-research/bigbird
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/byt5
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/active-qa
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
https://github.com/google/paxml
https://github.com/google/seqio
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/gooofy/zbrain
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/gouqi666/rast
https://github.com/gouwenrui/cpe-pro-main
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.