Task-oriented conversational agents rely on semantic parsers to translate
natural language to formal representations. In this paper, we propose the
design and rationale of the ThingTalk formal representation, and how the design
improves the development of transactional task-oriented agents.
ThingTalk is built on four core principles: (1) representing user requests
directly as executable statements, covering all the functionality of the agent,
(2) representing dialogues formally and succinctly to support accurate
contextual semantic parsing, (3) standardizing types and interfaces to maximize
reuse between agents, and (4) allowing multiple, independently-developed agents
to be composed in a single virtual assistant. ThingTalk is developed as part of
the Genie Framework that allows developers to quickly build transactional
agents given a database and APIs.
We compare ThingTalk to existing representations: SMCalFlow, SGD, TreeDST.
Compared to the others, the ThingTalk design is both more general and more
cost-effective. Evaluated on the MultiWOZ benchmark, using ThingTalk and
associated tools yields a new state of the art accuracy of 79% turn-by-turn.

本文提出了一种基于四个核心原则的 ThingTalk 正式表示法，用于改善交易任务导向代理的开发，与现有表示法相比，ThingTalk 设计更为通用且更具成本效益，使用 ThingTalk 和相关工具进行评估，可实现 79％的逐轮新的状态艺术精度。

ThingTalk：一个任务导向对话的可扩展、可执行表示语言

ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues

Grounding natural language instructions on the web to perform previously
unseen tasks enables accessibility and automation. We introduce a task and
dataset to train AI agents from open-domain, step-by-step instructions
originally written for people. We build RUSS (Rapid Universal Support Service)
to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with
pointers parses instructions to ThingTalk, a domain-specific language we design
for grounding natural language on the web. Then, a grounding model retrieves
the unique IDs of any webpage elements requested in ThingTalk. RUSS may
interact with the user through a dialogue (e.g. ask for an address) or execute
a web operation (e.g. click a button) inside the web runtime. To augment
training, we synthesize natural language instructions mapped to ThingTalk. Our
dataset consists of 80 different customer service problems from help websites,
with a total of 741 step-by-step instructions and their corresponding actions.
RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single
instructions. It outperforms state-of-the-art models that directly map
instructions to actions without ThingTalk. Our user study shows that RUSS is
preferred by actual users over web navigation.

介绍了一个训练 AI 智能体对象执行开放域、逐步指导的任务的数据集，该数据集包含 741 个逐步指令。此外，构建了一个名为 RUSS 的模型来处理此问题，并且它优于不使用 ThingTalk 直接映射指令的最新模型。

将开放域的指令应用于自动化 Web 支持任务

Grounding Open-Domain Instructions to Automate Web Support Tasks

Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ)
conversations suffer from the difficulty in acquiring a high-quality, manually
annotated training set. Approaches based only on dialogue synthesis are
insufficient, as dialogues generated from state-machine based models are poor
approximations of real-life conversations. Furthermore, previously proposed
dialogue state representations are ambiguous and lack the precision necessary
for building an effective agent. This paper proposes a new dialogue
representation and a sample-efficient methodology that can predict precise
dialogue states in WOZ conversations. We extended the ThingTalk representation
to capture all information an agent needs to respond properly. Our training
strategy is sample-efficient: we combine (1) fewshot data sparsely sampling the
full dialogue space and (2) synthesized data covering a subset space of
dialogues generated by a succinct state-based dialogue model. The completeness
of the extended ThingTalk language is demonstrated with a fully operational
agent, which is also used in training data synthesis. We demonstrate the
effectiveness of our methodology on MultiWOZ 3.0, a reannotation of the
MultiWOZ 2.1 dataset in ThingTalk. ThingTalk can represent 98% of the test
turns, while the simulator can emulate 85% of the validation set. We train a
contextual semantic parser using our strategy, and obtain 79% turn-by-turn
exact match accuracy on the reannotated test set.

本文提出了一种新的对话表示和高效的方法，可以在 Wizard-of-Oz (WOZ) 对话中预测精确的对话状态，使用 ThingTalk 表示法表示了所有代理需要回应的信息，并在 MultiWOZ 3.0 上验证了该方法，获得了 79% 的精确匹配准确度。