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Found 5 papers, 1 papers with code
Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text
They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text.
Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations
To tailor a learning system to the student’s level and needs, we must consider the characteristics of the learning content, such as its difficulty.
Active Learning to Guide Labeling Efforts for Question Difficulty Estimation
The experiments demonstrate that active learning with PowerVariance acquisition achieves a performance close to fully supervised models after labeling only 10% of the training data.
An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM
We present a visualisation tool which aims to illuminate the inner workings of an LSTM model for question answering.
Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques
As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).
