Learning human languages is a difficult task for a computer. However, Deep Learning (DL)
techniques have enhanced performance significantly for almost all-natural language …

Open-domain textual question answering (QA), which aims to answer questions from large
data sources like Wikipedia or the web, has gained wide attention in recent years. Recent …

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 …

Abstract Machine reading comprehension (MRC) is an AI challenge that requires machines
to determine the correct answers to questions based on a given passage. MRC systems …

We consider the problem of adapting neural paragraph-level question answering models to
the case where entire documents are given as input. Our proposed solution trains models to …

Existing work on augmenting question answering (QA) models with external knowledge (eg,
knowledge graphs) either struggle to model multi-hop relations efficiently, or lack …

Reading comprehension QA tasks have seen a recent surge in popularity, yet most works
have focused on fact-finding extractive QA. We instead focus on a more challenging multi …

Understanding narratives requires reasoning about implicit world knowledge related to the
causes, effects, and states of situations described in text. At the core of this challenge is how …

We introduce a neural reading comprehension model that integrates external commonsense
knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only …

Obtaining training data for Question Answering (QA) is time-consuming and resource-
intensive, and existing QA datasets are only available for limited domains and languages. In …