https://github.com/ussiamaboat/research-paper
공식 코드Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
"You are grounded!": Latent Name Artifacts in Pre-trained Language Models
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
An Information-Theoretic Approach to Analyze NLP Classification Tasks
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Zero-Shot Complex Question-Answering on Long Scientific Documents
Knowing-how & Knowing-that: A New Task for Machine Comprehension of User Manuals
Inquisitive Question Generation for High Level Text Comprehension
Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages
VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
Interactive Machine Comprehension with Dynamic Knowledge Graphs
Interactive Machine Comprehension with Information Seeking Agents
Learning to Ask: Neural Question Generation for Reading Comprehension
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals
Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.