In schema-guided dialogue state tracking models estimate the current state of
a conversation using natural language descriptions of the service schema for
generalization to unseen services. Prior generative approaches which decode
slot values sequentially do not generalize well to variations in schema, while
discriminative approaches separately encode history and schema and fail to
account for inter-slot and intent-slot dependencies. We introduce SPLAT, a
novel architecture which achieves better generalization and efficiency than
prior approaches by constraining outputs to a limited prediction space. At the
same time, our model allows for rich attention among descriptions and history
while keeping computation costs constrained by incorporating linear-time
attention. We demonstrate the effectiveness of our model on the Schema-Guided
Dialogue (SGD) and MultiWOZ datasets. Our approach significantly improves upon
existing models achieving 85.3 JGA on the SGD dataset. Further, we show
increased robustness on the SGD-X benchmark: our model outperforms the more
than 30$\times$ larger D3ST-XXL model by 5.0 points.

通过自然语言描述对话模式，采用 SPLAT 模型实现对话状态跟踪，提高泛化性和效率。

基于跨度选择的线性注意力变换器用于有效和健壮的基于模式指导的对话状态跟踪

Span-Selective Linear Attention Transformers for Effective and Robust  Schema-Guided Dialogue State Tracking

Dialog State Tracking (DST) is one of the most crucial modules for
goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast
Schema Guided Tracker), a fast and robust BERT-based model for state tracking
in goal-oriented dialogue systems. The proposed model is designed for the
Schema-Guided Dialogue (SGD) dataset which contains natural language
descriptions for all the entities including user intents, services, and slots.
The model incorporates two carry-over procedures for handling the extraction of
the values not explicitly mentioned in the current user utterance. It also uses
multi-head attention projections in some of the decoders to have a better
modelling of the encoder outputs. In the conducted experiments we compared
FastSGT to the baseline model for the SGD dataset. Our model keeps the
efficiency in terms of computational and memory consumption while improving the
accuracy significantly. Additionally, we present ablation studies measuring the
impact of different parts of the model on its performance. We also show the
effectiveness of data augmentation for improving the accuracy without
increasing the amount of computational resources.

本文介绍了一种基于 BERT 的快速和强健的模型 FastSGT 用于在目标导向对话系统中跟踪对话状态，在实验中，FastSGT 在保持计算和内存消耗效率的同时，显着提高了准确性，并通过数据增强显示了提高准确性的有效性。

基于 BERT 的快速而健壮的基于模式引导的对话数据集跟踪器

A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided  Dialogue Dataset

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。