Dialogue-AMR Annotation Guidelines
Abstract
This report describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding in dialogue systems. We explore dialogue in several domains of human-robot interaction, where a conversational robot is engaged in collaborative tasks with a human partner. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speakers intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. To address these limitations, we develop an inventory of speech acts suitable for our domain, and present Dialogue-AMR, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 2023
- Accession Number
- AD1194325
Entities
People
- Austin Blodgett
- Claire Bonial
- Clare Voss
- David R Traum
- Lucia Donatelli
- Mitchell Abrams
- Taylor Hudson
Organizations
- Florida Institute for Human and Machine Cognition
- Oak Ridge Associated Universities
- Saarland University
- Tufts University
- United States Army
- University of Southern California