A universal model for flexible item selection in conversational dialogs

Human-computer interaction and statistical natural language understanding has changed with the addition of a visual display screen in modern mobile devices, as visual rendering is used to communicate the dialog system's response. Onscreen item identification and resolution when interpreting the user utterances is one critical problem to achieve the natural and accurate human-machine communication. This problem, also called Flexible Item Selection (FIS), has been posed as a classification task to correctly identify intended on-screen item(s) from user utterances. This paper presents a universal FIS model that can be applied to dialog systems developed in different languages. We design a set of input features for the FIS model that makes it largely language-independent. We demonstrate that a single universal FIS model can be used in place of language specific FIS models with no loss in accuracy. We also show that such a model can generalize well to new unseen languages with minimal loss in accuracy on held out languages including English, French, Spanish, Italian, German, and Chinese. Eliminating the need for building and maintaining a separate FIS model for each new language, the universal FIS model helps scaling an existing dialogue system to new languages faster at a lower development cost.