Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models

TL;DR

This paper introduces a hierarchical hidden Markov model framework with expression sharing for weakly supervised semantic frame induction, enabling efficient learning from user-specific utterances without prior alignment or complete frame information.

Contribution

The paper proposes structural modifications and an extension called expression sharing to improve semantic frame induction efficiency in weakly supervised settings using hierarchical HMMs.

Findings

Positive effects of system extensions on accuracy

High accuracy achieved with limited training data

Effective on both orthographic and phonetic inputs

Abstract

We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface. The system is trained on an individual user's utterances and the corresponding semantic frames representing controls. During training, no prior information on the alignment between utterance segments and frame slots and values is available. In addition, semantic frames in the training data can contain information that is not expressed in the utterances. To tackle this weakly supervised classification task, we propose a framework based on Hidden Markov Models (HMMs). Structural modifications, resulting in a hierarchical HMM, and an extension called expression sharing are introduced to minimize the amount of training time and effort required for the user. The dataset used for the present study is PATCOR, which contains commands uttered in the context of a vocally guided card game, Patience. Experiments were carried out on orthographic and phonetic transcriptions of commands, segmented on different levels of n-gram granularity. The experimental results show positive effects of all the studied system extensions, with some effect differences between the different input representations. Moreover, evaluation experiments on held-out data with the optimal system configuration show that the extended system is able to achieve high accuracies with relatively small amounts of training data.