Impact of External Knowledge Graphs and Data Ratios on Zero-Shot Intent Classification in Hybrid Batch Training

Abstract

Abstract: Multi-label topic classification without labeled training data is a challenging task, specially when documents contain complex relational information. We present a zero-shot multi-label topic classification framework and systematically investigate how per-article knowledge graph augmentation affects its performance. The base framework classifies topics in documents without labeled training data and has four variants: article-only classification, keyword-enhanced classification, and self-consistency decoding variants of both. Then, we augment each base variant with per article knowledge graph.

Research Question

How does the integration of external knowledge graphs affect zero-shot intent classification accuracy on the MTOP benchmark when combined with monolingual-to-multilingual data ratio variations in hybrid batch training?

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