  In many scenarios of a language identification task, the user will specify a
small set of languages which he/she can speak instead of a large set of all
possible languages. We want to model such prior knowledge into the way we train
our neural networks, by replacing the commonly used softmax loss function with
a novel loss function named tuplemax loss. As a matter of fact, a typical
language identification system launched in North America has about 95% users
who could speak no more than two languages. Using the tuplemax loss, our system
achieved a 2.33% error rate, which is a relative 39.4% improvement over the
3.85% error rate of standard softmax loss method.
