Building curious machines that can answer as well as ask questions is an important
challenge for AI. The two tasks of question answering and question generation are usually …

Cognitive science researchers have emphasized the importance of ordering a complex task
into a sequence of easy to hard problems. Such an ordering provides an easier path to …

We focus on multiple-choice question answering (QA) tasks in subject areas such as
science, where we require both broad background knowledge and the facts from the given …

Textbooks are rich sources of information. Harvesting structured knowledge from textbooks
is a key challenge in many educational applications. As a case study, we present an …

We study reinforcement learning of chatbots with recurrent neural network architectures
when the rewards are noisy and expensive to obtain. For instance, a chatbot used in …

Recently, pretrained language models (eg, BERT) have achieved great success on many
downstream natural language understanding tasks and exhibit a certain level of …

We present the novel task of understanding multi-sentence entity-seeking questions
(MSEQs), that is, the questions that may be expressed in multiple sentences, and that expect …

In the domain of question subjectivity classification, there exists a need for detailed datasets
that can foster advancements in Automatic Subjective Question Answering (ASQA) systems …

For many applications of question answering (QA), being able to explain why a given model
chose an answer is critical. However, the lack of labeled data for answer justifications makes …

This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer),
a novel method for the automatic transfer of explanatory knowledge through neural …