9 citations · 15 across the 3 of their papers we have counts for
5 papers
What Makes Data-to-Text Generation Hard for Pretrained Language Models?
Moniba Keymanesh, Adrian Benton, Mark Dredze
Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Prev…
Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach
Moniba Keymanesh, Micha Elsner, Srinivasan Parthasarathy
Existing work on making privacy policies accessible has explored new presentation forms such as color-coding based on the risk factors or summarization to assist users with conscio…
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths
Ritesh Sarkhel, Moniba Keymanesh, Arnab Nandi +1
Abstractive summarization at controllable lengths is a challenging task in natural language processing. It is even more challenging for domains where limited training data is avail…
Twitter Watch: Leveraging Social Media to Monitor and Predict Collective-Efficacy of Neighborhoods
Moniba Keymanesh, Saket Gurukar, Bethany Boettner +3
Sociologists associate the spatial variation of crime within an urban setting, with the concept of collective efficacy. The collective efficacy of a neighborhood is defined as soci…
Network Representation Learning: Consolidation and Renewed Bearing
Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan +9
Graphs are a natural abstraction for many problems where nodes represent entities and edges represent a relationship across entities. An important area of research that has emerged…