Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text

no code implementations • RANLP 2021 • Ekaterina Loginova, Luca Benedetto, Dries Benoit, Paolo Cremonesi

They use questions of known difficulty to train models capable of inferring the difficulty of questions from their text.

Question Answering

Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

no code implementations • NAACL (BEA) 2022 • Ekaterina Loginova, Dries Benoit

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.

An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM

no code implementations • EMNLP 2018 • Ekaterina Loginova, G{\"u}nter Neumann

We present a visualisation tool which aims to illuminate the inner workings of an LSTM model for question answering.

Feature Engineering Machine Translation +1

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations • WS 2018 • Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

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).

Question Answering Sentence