Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations

TL;DR

This paper introduces a novel contrastive learning framework with multi-level data augmentation to improve slot filling models' robustness against Out-of-Vocabulary issues in dialogue systems.

Contribution

It proposes a unified contrastive learning approach combined with multi-level data augmentation to enhance OOV robustness in slot filling tasks.

Findings

Outperforms previous state-of-the-art methods on two datasets.

Effectively handles OOV words and slots in slot filling.

Demonstrates improved generalization in OOV scenarios.

Abstract

In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems. To address this issue, we propose an OOV robust slot filling model based on multi-level data augmentations to solve the OOV problem from both word and slot perspectives. We present a unified contrastive learning framework, which pull representations of the origin sample and augmentation samples together, to make the model resistant to OOV problems. We evaluate the performance of the model from some specific slots and carefully design test data with OOV word perturbation to further demonstrate the effectiveness of OOV words. Experiments on two datasets show that our approach outperforms the previous sota methods in terms of both OOV slots and words.