Dialogue State Tracking (DST) is a core component of virtual assistants such
as Alexa or Siri. To accomplish various tasks, these assistants need to support
an increasing number of services and APIs. The Schema-Guided State Tracking
track of the 8th Dialogue System Technology Challenge highlighted the DST
problem for unseen services. The organizers introduced the Schema-Guided
Dialogue (SGD) dataset with multi-domain conversations and released a zero-shot
dialogue state tracking model. In this work, we propose a GOaL-Oriented
Multi-task BERT-based dialogue state tracker (GOLOMB) inspired by architectures
for reading comprehension question answering systems. The model "queries"
dialogue history with descriptions of slots and services as well as possible
values of slots. This allows to transfer slot values in multi-domain dialogues
and have a capability to scale to unseen slot types. Our model achieves a joint
goal accuracy of 53.97% on the SGD dataset, outperforming the baseline model.

本文提出了一种基于 BERT 模型的 GOaL-Oriented 多任务对话状态跟踪器（GOLOMB），该模型通过查询对话历史记录中的槽口和服务的描述以及可能的槽口值来传输多域对话中的槽口值，并具有扩展到未见过的槽口类型的能力。我们的模型在 SGD 数据集上取得了 53.97％的联合目标准确率，优于基线模型。

目标导向的多任务 BERT 对话状态跟踪器

Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker

Virtual assistants such as Google Assistant, Alexa and Siri provide a
conversational interface to a large number of services and APIs spanning
multiple domains. Such systems need to support an ever-increasing number of
services with possibly overlapping functionality. Furthermore, some of these
services have little to no training data available. Existing public datasets
for task-oriented dialogue do not sufficiently capture these challenges since
they cover few domains and assume a single static ontology per domain. In this
work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing
over 16k multi-domain conversations spanning 16 domains. Our dataset exceeds
the existing task-oriented dialogue corpora in scale, while also highlighting
the challenges associated with building large-scale virtual assistants. It
provides a challenging testbed for a number of tasks including language
understanding, slot filling, dialogue state tracking and response generation.
Along the same lines, we present a schema-guided paradigm for task-oriented
dialogue, in which predictions are made over a dynamic set of intents and
slots, provided as input, using their natural language descriptions. This
allows a single dialogue system to easily support a large number of services
and facilitates simple integration of new services without requiring additional
training data. Building upon the proposed paradigm, we release a model for
dialogue state tracking capable of zero-shot generalization to new APIs, while
remaining competitive in the regular setting.

本文介绍了 Schema-Guided Dialogue (SGD) 数据集，包括 16 个领域的 16,000 个多领域对话。SGD 数据集提供了一个具有挑战性的测试平台，可以用于测试任务包括语言理解，位置填充，对话状态跟踪和响应生成。文章也提出了一个基于模式的任务导向对话的范式，尤其是在输入中提供了一组动态的意图和插槽自然的语言描述，提高了对大量服务形态的支持和新服务的快速集成。