Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Machine reading comprehension (MRC), which requires a machine to answer questions
based on a given context, has attracted increasing attention with the incorporation of various …

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 …

There is an increasing interest in studying natural language and computer code together, as
large corpora of programming texts become readily available on the Internet. For example …

We present a new dataset for machine comprehension in the medical domain. Our dataset
uses clinical case reports with around 100,000 gap-filling queries about these cases. We …

Words in natural language follow a Zipfian distribution whereby some words are frequent but
most are rare. Learning representations for words in the" long tail" of this distribution …

A reverse dictionary takes the description of a target word as input and outputs the target
word together with other words that match the description. Existing reverse dictionary …

Commonsense and background knowledge is required for a QA model to answer many
nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a …

Common-sense and background knowledge is required to understand natural language, but
in most neural natural language understanding (NLU) systems, this knowledge must be …

This study focuses on acquisition of commonsense knowledge. A previous study proposed a
commonsense knowledge base completion (CKB completion) method that predicts a …