https://github.com/xufangzhi/logiformer
공식 코드Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers
Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension
Towards a Better Understanding Human Reading Comprehension with Brain Signals
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
Commonsense for Generative Multi-Hop Question Answering Tasks
Lite Unified Modeling for Discriminative Reading Comprehension
Reference Knowledgeable Network for Machine Reading Comprehension
Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Dataset for the First Evaluation on Chinese Machine Reading Comprehension
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Modelling Sentence Pairs with Tree-structured Attentive Encoder
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
New Intent Discovery with Pre-training and Contrastive Learning
Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation
TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.