Code-switched Language Models Using Dual RNNs and Same-Source Pretraining

Saurabh Garg; Tanmay Parekh; Preethi Jyothi

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arXiv · 1809.01962

Code-switched Language Models Using Dual RNNs and Same-Source Pretraining

Saurabh Garg·Tanmay Parekh·Preethi Jyothi

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Abstract

This work focuses on building language models (LMs) for code-switched text. We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data. We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.

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BibTeX RIS

Saurabh Garg, Tanmay Parekh, Preethi Jyothi. 2018-09-06. Code-switched Language Models Using Dual RNNs and Same-Source Pretraining. https://arxiv.org/abs/1809.01962

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