Language Detection Engine for Multilingual Texting on Mobile Devices
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More than 2 billion mobile users worldwide type in multiple languages in the soft keyboard. On a monolingual keyboard, 38 are valid in another language. This can be easily avoided by detecting the language of typed words and then validating it in its respective language. Language detection is a well-known problem in natural language processing. In this paper, we present a fast, light-weight and accurate Language Detection Engine (LDE) for multilingual typing that dynamically adapts to user intended language in real-time. We propose a novel approach where the fusion of character N-gram model and logistic regression based selector model is used to identify the language. Additionally, we present a unique method of reducing the inference time significantly by parameter reduction technique. We also discuss various optimizations fabricated across LDE to resolve ambiguity in input text among the languages with the same character pattern. Our method demonstrates an average accuracy of 94.5 for European languages on the code-switched data. This model outperforms fastText by 60.39 is faster on mobile device with an average inference time of 25.91 microseconds.
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