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

This 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.