The paper proposes an unsupervised approach for discovering dialogue flows from a history of task-oriented dialogues. The approach consists of three main steps:
Utterance Representation: Utterances are represented in a vector space using sentence embeddings to capture their semantic similarity.
Utterance Clustering: The utterances are clustered based on their semantic similarity, with the clusters representing dialogue states.
Flow Discovery: The discovered clusters are used as vertices in a transition graph, with edges representing the transitions between states and their corresponding probabilities, derived from the conversation history.
The approach is applied to the MultiWOZ 2.2 dataset, a well-known dataset of task-oriented dialogues. The resulting dialogue flows are visualized, providing insights into the common patterns and structures of the conversations. An automatic evaluation metric is also proposed to assess the reliability and accuracy of the discovered flows on unseen dialogues.
The key benefits of this approach are its unsupervised nature, making it applicable to any collection of task-oriented dialogues, and its potential to support the design, processing, and validation of dialogue systems, as well as the guidance of human agents in call centers.
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Önemli Bilgiler Şuradan Elde Edildi
by Patr... : arxiv.org 05-03-2024
https://arxiv.org/pdf/2405.01403.pdfDaha Derin Sorular