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/mineshmathew/DocVQA/tree/master/BERT_baseline
https://github.com/minstar/BIDAF
https://github.com/mirbostani/QA-KD-AL
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/mithunpaul08/bert_tensorflow
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mivg/sled
https://github.com/mkavim/finetune_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mnm-rnd/elsa-voice-asr
https://github.com/mocherson/aki_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mohamedateya/bert-fine-tuning-sentence-classification-for-cola
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/moon23k/Transformer_Archs
https://github.com/mpandeydev/SDnetmod
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/mrzjy/sunburst
https://github.com/msalhab96/Listen-Attend-and-Spell
https://github.com/msalhab96/SpeeQ
https://github.com/mthcom/hscore-dataset-pruning
https://github.com/mtzcorporations/nlp_teamjodka
https://github.com/musixmatchresearch/umberto
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/muu4649/word2vec_attention
https://github.com/myamamoto555/tf-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nachiketaa/BERT-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nachiketaa/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nageshsinghc4/deepwrap
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nalbarr/coursera-ai4med-course3
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nastasiaF/5DEEP
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness
https://github.com/nbansal90/bAbi_QA
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/ncoop57/gpt-code-clippy
https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama
https://github.com/neil-zeng/asr
https://github.com/nerdimite/bert-finetuning-webinar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/neuralmagic/lm-evaluation-harness
https://github.com/neuralmind-ai/coliee
https://github.com/newmast/QA-Deep-Learning
https://github.com/nguyenvulebinh/vietnamese-roberta
https://github.com/ni9elf/QANet
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.