Our goal is to develop and deploy a virtual
assistant health coach that can help patients
set realistic physical activity goals and live a
more active lifestyle. Since there is no publicly shared dataset of health coaching dialogues, the first phase of our research focused
on data collection. We hired a certified health
coach and 28 patients to collect the first round
of human-human health coaching interaction
which took place via text messages. This resulted in 2853 messages. The data collection
phase was followed by conversation analysis
to gain insight into the way information exchange takes place between a health coach and
a patient. This was formalized using two annotation schemas: one that focuses on the goals
the patient is setting and another that models
the higher-level structure of the interactions.
In this paper, we discuss these schemas and
briefly talk about their application for automatically extracting activity goals and annotating
the second round of data, collected with different health coaches and patients. Given the
resource-intensive nature of data annotation,
successfully annotating a new dataset automatically is key to answer the need for high quality, large datasets.
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Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus
This paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained
semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical category conceptualization
and set of spatial annotation tools built in the context of a multimodal corpus consisting of 185 3D structure-building dialogues
between a human architect and human builder in Minecraft. Minecraft provides a particularly beneficial spatial relation-elicitation
environment because it automatically tracks locations and orientations of objects and avatars in the space according to an absolute
Cartesian coordinate system. Through a two-step process of sentence-level and document-level annotation designed to capture implicit
information, we leverage these coordinates and bearings in the AMRs in combination with spatial framework annotation to ground the
spatial language in the dialogues to absolute space.
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- Award ID(s):
- 1764048
- NSF-PAR ID:
- 10179909
- Date Published:
- Journal Name:
- Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020),
- Page Range / eLocation ID:
- 4883–4892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
