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/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/zjunlp/SemEval2021Task4
공식 코드https://github.com/zyz0000/finbert-mrc
공식 코드https://github.com/zzshou/RCAM
공식 코드https://gitlab.uni-koblenz.de/obermaie/negationcognitivereasoning
공식 코드https://huggingface.co/TownsWu/PEG
공식 코드https://worksheets.codalab.org/worksheets/0x9a15a170809f4e2cb7940e1f256dee55
공식 코드https://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d
공식 코드Adversarial Examples for Evaluating Reading Comprehension Systems
https://worksheets.codalab.org/worksheets/0xd2fb12d9f637460db16c110b5d3f2ca5
공식 코드Question Answering by Reasoning Across Documents with Graph Convolutional Networks
https://worksheets.codalab.org/worksheets/0xd53d03a48ef64b329c16b9baf0f99b0c
공식 코드https://www.kaggle.com/ignazio/train-and-test-a-cnn
공식 코드https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/1wy/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
https://github.com/2023-MindSpore-1/ms-code-150
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-162
https://github.com/2023-MindSpore-1/ms-code-163
https://github.com/2023-MindSpore-1/ms-code-164
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-1/ms-code-218/tree/main/sdne
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2023-MindSpore-1/ms-code-219/tree/main/albert
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/2023-MindSpore-1/ms-code-56
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/llama
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/2023-MindSpore-4/Code6/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/deberta_v2
https://github.com/2024-MindSpore-1/Code3/tree/main/scBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/27182812/ChineseBERT_paddle
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
https://github.com/2miatran/Natural-Language-Processing
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/30stomercury/Automatic_Speech_Recognition
https://github.com/8023looker/med-rr
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
https://github.com/8023looker/versatune
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.