Cross-lingual Text Classification with Heterogeneous Graph Neural Network

• URL: http://arxiv.org/abs/2105.11246v1
• Date: Mon, 24 May 2021 12:45:42 GMT
• Title: Cross-lingual Text Classification with Heterogeneous Graph Neural Network
• Authors: Ziyun Wang, Xuan Liu, Peiji Yang, Shixing Liu, Zhisheng Wang
• Abstract summary: Cross-lingual text classification aims at training a classifier on the source language and transferring the knowledge to target languages. Recent multilingual pretrained language models (mPLM) achieve impressive results in cross-lingual classification tasks. We propose a simple yet effective method to incorporate heterogeneous information within and across languages for cross-lingual text classification.
• Score: 2.6936806968297913
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cross-lingual text classification aims at training a classifier on the source language and transferring the knowledge to target languages, which is very useful for low-resource languages. Recent multilingual pretrained language models (mPLM) achieve impressive results in cross-lingual classification tasks, but rarely consider factors beyond semantic similarity, causing performance degradation between some language pairs. In this paper we propose a simple yet effective method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks (GCN). In particular, we construct a heterogeneous graph by treating documents and words as nodes, and linking nodes with different relations, which include part-of-speech roles, semantic similarity, and document translations. Extensive experiments show that our graph-based method significantly outperforms state-of-the-art models on all tasks, and also achieves consistent performance gain over baselines in low-resource settings where external tools like translators are unavailable.

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