Semantic Parsing in Limited Resource Conditions
Zhuang Li
This thesis explores challenges in semantic parsing, specifically focusing on
scenarios with limited data and computational resources. It offers solutions
using techniques like automatic data curation, knowledge transfer, active
learning, and continual learning.
  For tasks with no parallel training data, the thesis proposes generating
synthetic training examples from structured database schemas. When there is
abundant data in a source domain but limited parallel data in a target domain,
knowledge from the source is leveraged to improve parsing in the target domain.
  For multilingual situations with limited data in the target languages, the
thesis introduces a method to adapt parsers using a limited human translation
budget. Active learning is applied to select source-language samples for manual
translation, maximizing parser performance in the target language. In addition,
an alternative method is also proposed to utilize machine translation services,
supplemented by human-translated data, to train a more effective parser.
  When computational resources are limited, a continual learning approach is
introduced to minimize training time and computational memory. This maintains
the parser's efficiency in previously learned tasks while adapting it to new
tasks, mitigating the problem of catastrophic forgetting.
  Overall, the thesis provides a comprehensive set of methods to improve
semantic parsing in resource-constrained conditions.