A Slot Is Not Built in One Utterance: Spoken Language Dialogs with
Sub-Slots
- URL: http://arxiv.org/abs/2203.10759v1
- Date: Mon, 21 Mar 2022 07:10:19 GMT
- Title: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with
Sub-Slots
- Authors: Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun,
Caixia Yuan and Xiaojie Wang
- Abstract summary: This paper defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD)
The dataset includes a total of 40K dialogs and 500K utterances from four different domains: Chinese names, phone numbers, ID numbers and license plate numbers.
We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.
- Score: 67.69407159704328
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A slot value might be provided segment by segment over multiple-turn
interactions in a dialog, especially for some important information such as
phone numbers and names. It is a common phenomenon in daily life, but little
attention has been paid to it in previous work. To fill the gap, this paper
defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD) and builds
a Chinese dialog dataset SSD for boosting research on SSTOD. The dataset
includes a total of 40K dialogs and 500K utterances from four different
domains: Chinese names, phone numbers, ID numbers and license plate numbers.
The data is well annotated with sub-slot values, slot values, dialog states and
actions. We find some new linguistic phenomena and interactive manners in SSTOD
which raise critical challenges of building dialog agents for the task. We test
three state-of-the-art dialog models on SSTOD and find they cannot handle the
task well on any of the four domains. We also investigate an improved model by
involving slot knowledge in a plug-in manner. More work should be done to meet
the new challenges raised from SSTOD which widely exists in real-life
applications. The dataset and code are publicly available via
https://github.com/shunjiu/SSTOD.
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