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MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems
Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees.
Towards Tractable Mathematical Reasoning: Challenges, Strategies, and Opportunities for Solving Math Word Problems
Mathematical reasoning would be one of the next frontiers for artificial intelligence to make significant progress.
Knowledge-intensive Language Understanding for Explainable AI
To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.
Decision Making
Explainable Artificial Intelligence (XAI)
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KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding
We take BERT as a baseline model and implement the "Knowledge-Infused BERT" by infusing knowledge context from ConceptNet and WordNet, which significantly outperforms BERT and other recent knowledge-aware BERT variants like ERNIE, SenseBERT, and BERT_CS over eight different subtasks of GLUE benchmark.
Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.
A framework for predicting, interpreting, and improving Learning Outcomes
We develop an Embibe Score Quotient model (ESQ) to predict test scores based on observed academic, behavioral and test-taking features of a student.
