Conversation Management in Task-oriented Turkish Dialogue Agents with Dialogue Act Classification

O. Fatih Kilic, Enes B. Dundar, Yusufcan Manav, Tolga Cekic, Onur Deniz

Abstract

We study the problem of dialogue act classification to be used in conversation management of goal-oriented dialogue systems. Online chat behavior in human-machine dialogue systems differs from human-human spoken conversations. To this end, we develop 9 dialogue act classes by observing real-life human conversations from a banking domain Turkish dialogue agent. We then propose a dialogue policy based on these classes to correctly direct the users to their goals in a chatbot-human support hybrid dialogue system. To train a dialogue act classifier, we annotate a corpus of human-machine dialogues consisting of 426 conversations and 5020 sentences. Using the annotated corpus, we train a self-attentive bi-directional LSTM dialogue act classifier, which achieves 0.90 weighted F1-score on a sentence level classification performance. We deploy the trained model in the conversation manager to maintain the designed dialogue policy.

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