Towards Understanding Egyptian Arabic Dialogues
This work addresses the challenge of understanding spontaneous Egyptian Arabic dialogues in call-centers, which is an incremental improvement for domain-specific applications.
The paper tackles the problem of Dialogue Act classification for Egyptian Arabic dialogues by proposing a machine learning approach that does not rely on lexicons or rules, achieving an overall F1 score of 70.36% across three domains using a manually annotated corpus of 4725 utterances.
Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains.