Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …

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 …

Fine-tuning language models~(LMs) on human-generated data remains a prevalent
practice. However, the performance of such models is often limited by the quantity and …

Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Recently, a large number of studies focus on semantically or syntactically …

The problem of verifying whether a textual hypothesis holds based on the given evidence,
also known as fact verification, plays an important role in the study of natural language …

Abstract Machine learning research has advanced in multiple aspects, including model
structures and learning methods. The effort to automate such research, known as AutoML …

We consider open-domain queston answering (QA) where answers are drawn from either a
corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in …

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-
to-end deep neural networks. Specialized neural models have been developed for …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …