Recovering From Parser Failures: A Hybrid Statistical/Symbolic Approach

TL;DR

This paper presents a hybrid statistical and symbolic method for repairing parser failures in speech translation, improving accuracy by generating and refining meaning representations through interactive hypotheses.

Contribution

It introduces a novel hybrid approach that combines statistical and symbolic techniques for parser failure repair, with an adaptive model that learns over time.

Findings

Improved parser failure recovery in speech translation systems

Adaptive model enhances performance with use

Combines statistical likelihood with symbolic coherence

Abstract

We describe an implementation of a hybrid statistical/symbolic approach to repairing parser failures in a speech-to-speech translation system. We describe a module which takes as input a fragmented parse and returns a repaired meaning representation. It negotiates with the speaker about what the complete meaning of the utterance is by generating hypotheses about how to fit the fragments of the partial parse together into a coherent meaning representation. By drawing upon both statistical and symbolic information, it constrains its repair hypotheses to those which are both likely and meaningful. Because it updates its statistical model during use, it improves its performance over time.