Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level
This work addresses the problem of understanding spontaneous Arabic dialogues for applications like call-centers, but it is incremental as it builds on existing methods with specific data.
The paper tackled dialogue act classification for spontaneous Arabic speech and instant messages by proposing a statistical model with a hierarchical structure, achieving an average F-measure of 0.912 and improving it by approximately 20%.
The ability to model and automatically detect dialogue act is an important step toward understanding spontaneous speech and Instant Messages. However, it has been difficult to infer a dialogue act from a surface utterance because it highly depends on the context of the utterance and speaker linguistic knowledge; especially in Arabic dialects. This paper proposes a statistical dialogue analysis model to recognize utterance's dialogue acts using a multi-classes hierarchical structure. The model can automatically acquire probabilistic discourse knowledge from a dialogue corpus were collected and annotated manually from multi-genre Egyptian call-centers. Extensive experiments were conducted using Support Vector Machines classifier to evaluate the system performance. The results attained in the term of average F-measure scores of 0.912; showed that the proposed approach has moderately improved F-measure by approximately 20%.