Papers by Isabel Groves
Semantic Parsing of Disfluent Speech (2021.eacl-main)
Copied to clipboard
| Challenge: | Semantic parsing is a key component for understanding user utterances in voice assistants . however, most research on disfluent speech is focused on written text . |
| Approach: | They investigate semantic parsing of disfluent speech with the ATIS dataset . they add real and synthetic disfluencies at training time to improve model performance . |
| Outcome: | The proposed parser outperforms the state-of-the-art parsers on the ATIS dataset in terms of performance and accuracy. |
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
Copied to clipboard
| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing (2022.aacl-short)
Copied to clipboard
| Challenge: | Large Language Models excel at a low-resource level given limited data, but are unsuitable for runtime systems which require low latency. |
| Approach: | They propose a method to augment training data for a model 40x smaller (500M parameters) they use Alexa to generate synthetic data from Alexa 20B to augment the training set . |
| Outcome: | The proposed method improves low-resource SP on two datasets in low-source settings. |