Siamese Networks for Inference in Malayalam Language Texts

Abstract

Natural language inference is a method of finding inferences in language texts. Understanding the meaning of a sentence and its inference is essential in many language processing applications. In this context, we consider the inference problem for a Dravidian language, Malayalam. Siamese networks train the text hypothesis pairs with word embeddings and language agnostic embeddings, and the results are evaluated against classification metrics for binary classification into entailment and contradiction classes. XLM-R embeddings based Siamese architecture using gated recurrent units and bidirectional long short term memory networks provide promising results for this classification problem.

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