Search Results for author:
Found 4 papers, 3 papers with code
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
Other results reveal that our probing questions can help to improve the performance of the models (e. g., by +10. 3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension
The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years.
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
