Creating a low resource semantic parser for the unified meaning representation format

dc.contributor.advisorLandsberger, Sheldon
dc.contributor.advisorPryor, Mitchell Wayne
dc.creatorWanna, Selma Liliane
dc.date.accessioned2021-08-30T21:20:21Z
dc.date.available2021-08-30T21:20:21Z
dc.date.created2021-05
dc.date.issued2021-04-30
dc.date.submittedMay 2021
dc.date.updated2021-08-30T21:20:22Z
dc.description.abstractThis thesis investigates the performance of state-of-the-art neural models on a low resource semantic parsing task. This task required the models to convert natural language commands directed at a robot into Unified Meaning Representation Format (UMRF) structures. UMRF structures are standalone Meaning Representation (MR) containers that support embedding predicate-argument semantics and graphical MR formats. The structure was design for semi-autonomous systems in Human Robot Interaction (HRI) domains. The UMRF formalism is both new and novel, thus there is a scarcity of annotated UMRF data and thus a lack of available training data. For this project, the Examine in light task from the ALFRED dataset was selected as the corpora to annotate labeled UMRF training and validation examples. 1,010 and 100 training and validation datasets were collected respectively. Thereafter, the following models were tested on the low resource semantic parsing task: sequence-to-sequence, CopyNet, and transformer architectures. Of the three designs, the CopyNet model performed the best with a BLEU score of 0.891 and an accuracy of 61.3%. Once the design was finalized, the CopyNet model was integrated into a ROS2 software package, allowing the larger robotics community to access the semantic parser.
dc.description.departmentMechanical Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/87273
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/14223
dc.language.isoen
dc.subjectNatural language processing
dc.subjectSemantic parsing
dc.subjectLow resource
dc.subjectMachine learning
dc.subjectRobotics
dc.titleCreating a low resource semantic parser for the unified meaning representation format
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
WANNA-THESIS-2021.pdf
Size:
7.2 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.45 KB
Format:
Plain Text
Description:
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.84 KB
Format:
Plain Text
Description: