25/07/2020

Attending to inter-sentential features in neural text classification

Billy Chiu, Sunil Kumar Sahu, Neha Sengupta, Derek Thomas, Mohammady Mahdy

Keywords: graph network, hybrid neural network, attention mechanism

Abstract Paper Similar Papers

Abstract: Text classification requires a deep understanding of the linguistic features in text; in particular, the intra-sentential (local) and inter-sentential features (global). Models that operate on word sequences have been successfully used to capture the local features, yet they are not effective in capturing the global features in long-text. We investigate graph-level extensions to such models and propose a novel architecture for combining alternative text features. It uses an attention mechanism to dynamically decide how much information to use from a sequence- or graph-level component. We evaluated different architectures on a range of text classification datasets, and graph-level extensions were found to improve performance on most benchmarks. In addition, the attention-based architecture, as adaptively-learned from the data, outperforms the generic and fixed-value concatenation ones.

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