Conversational agents are consistently growing in popularity and many people interact with them every day. While many conversational agents act as personal assistants, they can have many different goals. Some are task-oriented, such as providing customer support for a bank or making a reservation. Others are designed to be empathetic and to form emotional connections with the user. The Alexa Prize Challenge aims to create a socialbot, which allows the user to engage in coherent conversations, on a range of popular topics that will interest the user. Here we describe Athena 2.0, UCSC's conversational agent for Amazon's Socialbot Grand Challenge 4. Athena 2.0 utilizes a novel knowledge-grounded discourse model that tracks the entity links that Athena introduces into the dialogue, and uses them to constrain named-entity recognition and linking, and coreference resolution. Athena 2.0 also relies on a user model to personalize topic selection and other aspects of the conversation to individual users.

翻译：对话代理的受欢迎程度持续增长，许多人每天与它们进行互动。尽管许多对话代理充当个人助理的角色，但它们可以服务于多种不同的目标。有些是任务导向型的，例如为银行提供客户支持或进行预约；另一些则被设计为具有同理心，旨在与用户建立情感联系。Alexa Prize挑战赛旨在打造一个社交机器人，使用户能够在一系列用户感兴趣的热门话题上展开连贯的对话。本文描述了UCSC为亚马逊社交机器人大赛第四赛季开发的对话代理Athena 2.0。Athena 2.0采用了一种新颖的基于知识的话语模型，该模型追踪Athena引入对话中的实体链接，并利用这些链接来约束命名实体识别与链接以及共指消解。此外，Athena 2.0还依赖用户模型来个性化主题选择及其他对话方面的内容，以适应不同用户的需求。