Knowledge graph (KG) question generation (QG) aims to generate natural language
questions from KGs and target answers. Previous works mostly focus on a simple setting that …

Generative commonsense reasoning (GCR) in natural language is to reason about the
commonsense while generating coherent text. Recent years have seen a surge of interest in …

The rapid development and application of natural language generation (NLG) techniques
has revolutionized the field of automatic text production. However, these techniques are still …

An important goal of online platforms is to enable content discovery, ie allow users to find a
catalog entity they were not familiar with. A pre-requisite to discover an entity, eg a book …

Diverse headline generation is an NLP task where given a news article, the goal is to
generate multiple headlines that are true to the content of the article but are different among …

Recent advances in commonsense reasoning have been fueled by the availability of large-
scale human annotated datasets. Manual annotation of such datasets, many of which are …

In the growing information retrieval (IR) world, selecting suitable keywords and generating
queries is important for effective retrieval. Modern database applications need a …

The integration of external knowledge graphs has emerged as a powerful approach to
enrich conversational AI systems with coherent and knowledge-rich conversations. This …

In specialized fields like the scientific domain, constructing largescale human-annotated
datasets poses a significant challenge due to the need for domain expertise. Recent …

In text generation, we aim to produce outputs that are not only correct but also diverse in
terms of content, use of words, and meaning. The ability to generate accurate and diverse …