Disentangled Representation for Long-tail Senses of Word Sense Disambiguation

Abstract

The long-tailed distribution, also called the heavy-tailed distribution, is common in nature. Since both words and their senses in natural language have long-tailed phenomenon in usage frequency, the Word Sense Disambiguation (WSD) task faces serious data imbalance. The existing learning strategies or data augmentation methods are difficult to deal with the lack of training samples caused by the single application scenario of long-tail senses, and the word sense representations caused by unique word sense definitions. Considering that the features extracted from the Disentangled Representation (DR) independently describe the essential properties of things, and DR does not require deep feature extraction and fusion processes, it alleviates the dependence of the representation learning on the training samples. We propose a novel DR by constraining the covariance matrix of a multivariate Gaussian distribution, which can enhance the strength of independence among features compared to β-VAE. The WSD model implemented by the reinforced DR outperforms the baselines on the English all-words WSD evaluation framework, the constructed long-tail word sense datasets, and the latest cross-lingual datasets.