Hybrid Batch Training Ratios in Knowledge Graph-Enhanced Cross-Lingual Topic Classification Models

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

What is the effect of hybrid batch training ratios on the convergence speed and final accuracy of knowledge graph-enhanced models for cross-lingual topic classification?

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