Multi-Party Dialogue Topic Tracking Response Selection
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Solution Overview
Problem
Existing retrieval-based response selection systems are limited to two-party conversations and struggle to effectively manage dynamic topic changes in multi-party, multi-turn dialogues, failing to provide relevant responses in complex conversational settings.
Innovation Solution
A multi-party dialogue system utilizing a pre-trained language model with a dynamic topic tracking mechanism, incorporating self-supervised learning and multi-task learning to embed topic information and jointly train response selection, topic prediction, and disentanglement tasks, enabling efficient encoding and response ranking across multiple topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If retrieval-based response generation engine is used, then response fluency and grammaticality are improved, but the system is limited to two-party conversations only
Solution Approach 1:
The patent segments the conversation management into separate topic tracks, where each track independently manages a specific conversation topic. This allows the system to handle multiple topics simultaneously in multi-party conversations while maintaining the retrieval-based response generation approach for fluent and grammatical responses.
Solution Approach 2:
The patent creates a universal response generation engine that can serve multiple conversational contexts simultaneously. By implementing a topic track mechanism, the same retrieval-based system can adapt to different conversational scenarios (two-party, multi-party, single-topic, multi-topic) without requiring separate specialized systems for each case.
2Device complexity
If existing retrieval-based response selection systems are used, then response selection is simplified, but they fail to manage dynamic topic changes in multi-party conversations
Solution Approach 1:
The patent introduces dynamic topic tracks that can create, merge, and delete based on real-time conversation analysis. The system dynamically adjusts the number and identity of active topic tracks according to the conversational context, enabling it to manage dynamic topic changes in multi-party conversations while maintaining a relatively simple retrieval-based architecture.
Solution Approach 2:
The patent introduces topic tracks as intermediary structures between the conversation inputs and the response generation engine. These topic tracks serve as mediators that organize and filter conversation information, allowing the simplified retrieval-based system to effectively handle complex multi-party dynamics without requiring complex dialogue management logic.
3Reliability
If topic information is embedded using self-supervised learning, then response relevance is improved, but training complexity increases
Solution Approach 1:
The patent implements self-supervised pre-training of the language model on general conversation data before fine-tuning on domain-specific tasks. This preliminary action learns universal language understanding and topic modeling capabilities that transfer to specific application domains, improving response relevance while distributing the training complexity across separate stages rather than requiring complex domain-specific training from scratch.
Data Source
AI summary
Embodiments described herein provide a dynamic topic tracking mechanism that tracks how the conversation topics change from one utterance to another and use the tracking information to rank candidate responses. A pre-trained language model may be used for response selection in the multi-party conversations, which consists of two steps: (1) a topic-based pre-training to embed topic information into the language model with self-supervised learning, and (2) a multi-task learning on the pretrained model by jointly training response selection and dynamic topic prediction and disentanglement tasks.