This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …

We study the problem of learning representations of entities and relations in knowledge
graphs for predicting missing links. The success of such a task heavily relies on the ability of …

Knowledge graph (KG) embedding is to embed components of a KG including entities and
relations into continuous vector spaces, so as to simplify the manipulation while preserving …

We introduce a general-purpose conditioning method for neural networks called FiLM:
Feature-wise Linear Modulation. FiLM layers influence neural network computation via a …

One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop
logical reasoning over the facts captured by a knowledge graph (KG). This problem is …

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base
completion. Our model ConvKB advances state-of-the-art models by employing a …

Abstract Query embedding (QE)---which aims to embed entities and first-order logical (FOL)
queries in low-dimensional spaces---has shown great power in multi-hop reasoning over …

Abstract Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their creation and …