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Project: Exploring the Characteristics of Multi-Party Dialogues


The project delves into the realm of Natural Language Processing (NLP) to analyze, understand, and derive insights from multi-party conversations. This study focuses on unraveling the distinct characteristics, complexities, and patterns within conversations involving more than two participants, aiming to enhance the comprehension and modeling of such dialogues using advanced NLP techniques.

The primary objectives of this project are as follows:

Data Collection and Annotation: Gathering diverse datasets containing multi-party dialogues from various sources such as forums, meetings, debates, and social media interactions. These datasets will be meticulously annotated to facilitate the identification of speaker turns, conversational flows, speech acts, sentiment, and other linguistic features crucial to multi-party conversations.

Dialogue Modeling and Analysis: Employing advanced NLP algorithms and techniques to model multi-party dialogues. This involves exploring methods for turn-taking, discourse coherence, participant roles, and the identification of conversational dynamics, interruptions, and topic shifts within these complex conversations.

Structural and Semantic Understanding: Investigating the structural and semantic elements present in multi-party dialogues. Analyzing the interdependencies between speakers, their contributions, and the contextual meaning within the conversation, enabling a deeper understanding of the underlying discourse structure and content.

Evaluation of NLP Models: Developing and fine-tuning NLP models specifically tailored for multi-party dialogues. Evaluating the performance of these models in tasks such as summarization, sentiment analysis, and entity recognition within the context of multi-party conversations.

Applications and Implications: Exploring potential applications of the research findings in various domains, such as improving chatbots, enhancing collaborative systems, refining conversational AI, and facilitating better automated understanding of multi-party interactions.

This project aims to contribute to the field of NLP by shedding light on the intricacies of multi-party dialogues, which differ significantly from dyadic conversations. Understanding these complexities could have far-reaching implications, influencing the development of more sophisticated NLP models and tools, ultimately contributing to more accurate and context-aware systems for analyzing and generating multi-party conversations across a wide range of applications.

Meng Fang
Secondary supervisor
Jiaxu Zhao
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