Paraphrasing Techniques for Maritime QA system

TL;DR

This paper explores paraphrasing techniques to generate large-scale training data for semantic parsers in maritime AI systems, aiming to improve human-machine communication with limited manual data.

Contribution

It introduces a novel approach to use paraphrasing methods for training semantic parsers with limited data in the maritime domain.

Findings

Effective paraphrasing improves training data quality.

Enhanced semantic parser performance with limited manual annotations.

Demonstrated approach on real-world maritime data.

Abstract

There has been an increasing interest in incorporating Artificial Intelligence (AI) into Defence and military systems to complement and augment human intelligence and capabilities. However, much work still needs to be done toward achieving an effective human-machine partnership. This work is aimed at enhancing human-machine communications by developing a capability for automatically translating human natural language into a machine-understandable language (e.g., SQL queries). Techniques toward achieving this goal typically involve building a semantic parser trained on a very large amount of high-quality manually-annotated data. However, in many real-world Defence scenarios, it is not feasible to obtain such a large amount of training data. To the best of our knowledge, there are few works trying to explore the possibility of training a semantic parser with limited manually-paraphrased data, in other words, zero-shot. In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.