Natural language systems often rely on a single, potentially ambiguous input to make one final prediction, which may simplify the problem but degrade end user experience. Instead of making predictions with the natural languagequery only, we ask the user for additional information using a small number of binary and multiple-choice questionsin order to better help users accomplish their goals while minimizing their effort . At each turn, our system decides between asking the most informative question or making the final classification prediction. Our approach enables bootstrapping the system using simple crowdsourcing annotations without expensive human-to-human interaction data. Evaluation demonstrates that our method substantially increases classification accuracy, while effectively balancing the number of questions with the improvement to final accuracy .
We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions . At each turn, our system decides between asking the most informative question or making the final classification prediction. The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowdsourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction .
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Natural language systems often rely on a single, potentially ambiguous input to make one final prediction, which may simplify the problem but degrade end user experience. Instead of making predictions with the natural languagequery only, we ask the user
We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks
R
a small number of binary and multiple-choice questionsin order to better help users accomplish their goals while minimizing their effort
binary or multi-choice questions
R
Our approach enables bootstrapping the system using simple crowdsourcing annotations without expensive human-to-human interaction data. Evaluation demonstrates that our method substantially increases classification accuracy, while effectively balancing the number of questions with the improvement to final accuracy
The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowdsourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction