Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

It remains an open question whether incorporating external knowledge benefits
commonsense reasoning while maintaining the flexibility of pretrained sequence models. To …

This paper provides an extensive overview of the use of knowledge graphs in the context of
Explainable Machine Learning. As of late, explainable AI has become a very active field of …

Neural language representation models such as BERT pre-trained on large-scale corpora
can well capture rich semantic patterns from plain text, and be fine-tuned to consistently …

Commonsense reasoning aims to empower machines with the human ability to make
presumptions about ordinary situations in our daily life. In this paper, we propose a textual …

Deep learning models perform poorly on tasks that require commonsense reasoning, which
often necessitates some form of world-knowledge or reasoning over information not …

There has been a growing interest in solving Visual Question Answering (VQA) tasks that
require the model to reason beyond the content present in the image. In this work, we focus …

Recent developments in pre-trained neural language modeling have led to leaps in
accuracy on common-sense question-answering benchmarks. However, there is increasing …

Commonsense knowledge, such as knowing that “bumping into people annoys them” or
“rain makes the road slippery”, helps humans navigate everyday situations seamlessly. Yet …

Non-extractive commonsense QA remains a challenging AI task, as it requires systems to
reason about, synthesize, and gather disparate pieces of information, in order to generate …