# A weakly supervised sequence tagging and grammar induction approach to semantic frame slot filling

## TL;DR

This paper presents a weakly supervised method for semantic frame slot filling that improves accuracy and induces interpretable grammars using hierarchical models and inductive techniques.

## Contribution

It introduces a novel combination of hierarchical hidden Markov models with discriminative and grammar induction approaches for slot filling.

## Key findings

- Significant F-score improvements without extra data
- Automatic induction of domain-specific, interpretable grammars
- Effective weakly supervised learning for semantic tasks

## Abstract

This paper describes continuing work on semantic frame slot filling for a command and control task using a weakly-supervised approach. We investigate the advantages of using retraining techniques that take the output of a hierarchical hidden markov model as input to two inductive approaches: (1) discriminative sequence labelers based on conditional random fields and memory-based learning and (2) probabilistic context-free grammar induction. Experimental results show that this setup can significantly improve F-scores without the need for additional information sources. Furthermore, qualitative analysis shows that the weakly supervised technique is able to automatically induce an easily interpretable and syntactically appropriate grammar for the domain and task at hand.