[Paper Review] Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level

AbdelRahim Elmadany, Sherif Abdou+1 more|arXiv (Cornell University)|May 30, 2018

Speech and dialogue systems4 citations

TL;DR

This paper proposes a hierarchical statistical dialogue act classification model for spontaneous Egyptian Arabic speech and instant messages using a multi-class structure and Support Vector Machines. Trained on a manually annotated multi-genre call-center corpus, the model achieves an average F-measure of 0.912, representing a ~20% improvement over baseline methods, demonstrating significant gains in understanding informal Arabic dialogue contexts.

ABSTRACT

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%.

Motivation & Objective

• To address the challenge of accurately classifying dialogue acts in spontaneous Arabic speech and instant messages, which are highly context-dependent and linguistically complex.
• To develop a statistical dialogue analysis model capable of learning probabilistic discourse patterns from real-world, multi-genre Arabic conversational data.
• To improve classification performance over existing methods by leveraging a hierarchical multi-class structure for dialogue acts.
• To evaluate the model's effectiveness using a manually annotated corpus collected from Egyptian call centers.
• To demonstrate that context-aware, statistically learned discourse knowledge enhances dialogue act recognition in low-resource, informal Arabic.

Proposed method

• The authors design a hierarchical multi-class dialogue act classification model that organizes dialogue acts into a structured taxonomy to improve classification granularity and context sensitivity.
• Features are extracted from utterances using linguistic and contextual cues, including lexical, syntactic, and discourse-level patterns relevant to Arabic dialects.
• A Support Vector Machine (SVM) classifier is employed to learn the mapping from utterance features to dialogue act labels within the hierarchical structure.
• The model is trained on a manually annotated corpus of 1,500 dialogues collected from multi-genre Egyptian call centers, covering diverse conversational contexts.
• Probabilistic discourse knowledge is automatically acquired from the corpus through statistical learning, enabling the model to generalize across informal speech patterns.
• The hierarchical structure allows for better handling of rare or ambiguous dialogue acts by leveraging parent-child relationships in the taxonomy.

Experimental results

Research questions

• RQ1 Can a hierarchical statistical model improve dialogue act classification performance for spontaneous Arabic speech and instant messages?
• RQ2 To what extent does incorporating context and discourse-level features enhance dialogue act recognition in informal Arabic?
• RQ3 How effective is the proposed model compared to baseline approaches in terms of F-measure and robustness across dialogue act types?
• RQ4 Does the use of a manually annotated, multi-genre corpus from Egyptian call centers lead to better generalization in dialogue act classification?
• RQ5 Can probabilistic discourse knowledge be effectively learned and applied to improve classification accuracy in low-resource Arabic dialogue systems?

Key findings

• The proposed model achieves an average F-measure of 0.912, representing a significant improvement over baseline methods.
• The performance gain corresponds to a moderate improvement of approximately 20% in F-measure compared to previous approaches.
• The hierarchical structure enhances classification by enabling better generalization across rare or ambiguous dialogue act categories.
• The use of a real-world, multi-genre corpus from Egyptian call centers contributes to the model's robustness and practical applicability.
• The SVM-based classifier effectively learns from the statistical patterns in the annotated data, demonstrating strong generalization on informal Arabic utterances.
• The results confirm that context-aware, statistically learned discourse knowledge significantly improves dialogue act recognition in spontaneous Arabic communication.