Lexicon-injected Semantic Parsing for Task-Oriented Dialog

TL;DR

This paper introduces a lexicon-injected semantic parser for task-oriented dialog that improves handling of unseen slot values and achieves state-of-the-art performance without retraining.

Contribution

It proposes a novel span-splitting representation and a lexicon-injected parser with slot disambiguation, enhancing adaptability to dynamic slot entries in dialog systems.

Findings

Achieved 87.62% accuracy on TOP dataset.

Demonstrated robustness to updated slot lexicons.

Outperformed existing parsing methods.

Abstract

Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents and slots as labels of nested tree nodes, has been proposed for parsing user utterances. Previous TOP parsing methods are limited on tackling unseen dynamic slot values (e.g., new songs and locations added), which is an urgent matter for real dialog systems. To mitigate this issue, we first propose a novel span-splitting representation for span-based parser that outperforms existing methods. Then we present a novel lexicon-injected semantic parser, which collects slot labels of tree representation as a lexicon, and injects lexical features to the span representation of parser. An additional slot disambiguation technique is involved to remove inappropriate span match occurrences from the lexicon. Our best parser produces a new state-of-the-art result (87.62%) on the TOP dataset, and demonstrates its adaptability to frequently updated slot lexicon entries in real task-oriented dialog, with no need of retraining.