8 citations · 16 across the 5 of their papers we have counts for
6 papers
MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems
Keyur Faldu, Amit Sheth, Prashant Kikani +1
Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees. These architectures varied from seq2seq models, including…
Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems
Keyur Faldu, Amit Sheth, Prashant Kikani +2
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress. The ongoing surge to solve math word problems (MWPs) and hence a…
Knowledge-intensive Language Understanding for Explainable AI
Amit Sheth, Manas Gaur, Kaushik Roy +1
AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will n…
KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding
Keyur Faldu, Amit Sheth, Prashant Kikani +1
Contextualized entity representations learned by state-of-the-art transformer-based language models (TLMs) like BERT, GPT, T5, etc., leverage the attention mechanism to learn the d…
A framework for predicting, interpreting, and improving Learning Outcomes
Chintan Donda, Sayan Dasgupta, Soma S Dhavala +2
It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform design…
Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
Manas Gaur, Keyur Faldu, Amit Sheth
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing mass…