Look-up and Adapt: A One-shot Semantic Parser

TL;DR

This paper introduces a semantic parser that adapts existing logical forms from similar seen utterances to handle out-of-domain inputs, significantly improving one-shot parsing performance.

Contribution

It proposes a memory-based, adaptive semantic parsing approach that generalizes to unseen utterances by leveraging and modifying stored logical forms, rather than generating from scratch.

Findings

Achieved up to 68.8% improvement in one-shot parsing accuracy.

Developed a data generation strategy for multi-domain utterance-logical form pairs.

Demonstrated effectiveness across different evaluation settings.

Abstract

Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited "supported" domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8% on one-shot parsing under two different evaluation settings compared to the baselines.