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/ltgoslo/norquad
공식 코드https://github.com/lucabenedetto/qdet-comparison
공식 코드A quantitative study of NLP approaches to question difficulty estimation
https://github.com/luciusssss/VGaokao
공식 코드Extract, Integrate, Compete: Towards Verification Style Reading Comprehension
https://github.com/luciusssss/how-many-answers
공식 코드How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
https://github.com/luciusssss/why-learn-shortcut
공식 코드https://github.com/luciusssss/xkbqa-as-mrc
공식 코드Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
https://github.com/luffycodes/MalAlgoQA-Dataset
공식 코드https://github.com/lujunru/rolemrc
공식 코드RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
https://github.com/lujunru/tranclr
공식 코드Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation
https://github.com/luka-group/causal-view-of-entity-bias
공식 코드https://github.com/luohongyin/RGX
공식 코드https://github.com/luoxiaoheics/continual-tune
공식 코드An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
https://github.com/lxucs/multilingual-mrc-isdg
공식 코드Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
https://github.com/lyingcs/kuaicomt.github.io
공식 코드Comment Staytime Prediction with LLM-enhanced Comment Understanding
https://github.com/lyingcs/lcu
공식 코드Comment Staytime Prediction with LLM-enhanced Comment Understanding
https://github.com/maafiah/InferenceQuestionsAQG
공식 코드Automatic Generation of Inference Making Questions for Reading Comprehension Assessments
https://github.com/magiccircuit/enhance-finegrained
공식 코드https://github.com/mainlp/dialect-tod-robustness
공식 코드Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varieties
https://github.com/maitrix-org/de-arena
공식 코드Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models
https://github.com/mataney/APES
공식 코드Question Answering as an Automatic Evaluation Metric for News Article Summarization
https://github.com/mataney/APES-optimizer
공식 코드Question Answering as an Automatic Evaluation Metric for News Article Summarization
https://github.com/matnlp/cmedmrc
공식 코드https://github.com/maxbartolo/adversarialQA
공식 코드Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
https://github.com/mbzuai-nlp/multichoice2extractiveqa
공식 코드From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
https://github.com/mega002/DocQN
공식 코드Learning to Search in Long Documents Using Document Structure
https://github.com/mega002/qdmr-based-question-generation
공식 코드Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition
https://github.com/mhany90/Multi-WikiRE
공식 코드X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
https://github.com/mhardalov/bg-reason-BERT
공식 코드https://github.com/microsoft/DeBERTa
공식 코드https://github.com/microsoft/DeepSpeed
공식 코드The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
https://github.com/microsoft/lmops
공식 코드Adapting Large Language Models to Domains via Reading Comprehension
https://github.com/microsoft/moonlit
공식 코드Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
https://github.com/microsoft/promptbench
공식 코드PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
https://github.com/microsoft/scglab
공식 코드https://github.com/minhquan6203/vitextvqa-dataset
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.