Multi-party Dialogue Discourse Parsing with Sequence Identifiers

The patent applies preliminary action by pre-training the language model on general corpus data before fine-tuning on the specific discourse parsing task. This pre-training phase prepares the model with general linguistic knowledge and discourse patterns, reducing the amount of task-specific data needed for effective fine-tuning. The model is preliminarily equipped with discourse relation understanding through pre-training on datasets like RST, enabling better performance with limited annotated data.

The patent implements universality by designing a language model that can handle multiple discourse parsing tasks and formats through a unified framework. The same pre-trained model serves different discourse relation types (e.g., nuclear, satellite, transition relations) and can be applied to various dialogue scenarios without requiring separate models for each task, thereby reducing overall data requirements across multiple applications.

Instead of using randomly initialized classifiers, the patent applies preliminary action by pre-training the encoder on large-scale corpora to learn robust feature representations. This pre-training establishes meaningful initial weights in the encoder that capture linguistic patterns and discourse structures, eliminating the need for extensive task-specific training data that would otherwise be required to initialize classifiers from scratch.

The patent implements self-service by enabling the pre-trained encoder to provide its own feature representations without relying on externally trained classifiers. The encoder itself serves as the feature extractor, leveraging its pre-trained knowledge to generate meaningful representations that can be directly used for discourse parsing tasks, thereby eliminating the separate classifier training process that consumes additional data.

The patent applies segmentation by breaking down multi-party dialogue discourse parsing into distinct components: identifying discourse units (DUs), determining discourse relations between DUs, and classifying relation types. This segmented approach allows the model to focus on specific aspects of discourse structure separately, improving overall analysis accuracy by addressing each component with targeted processing rather than treating the entire dialogue as a single complex task.

The patent implements another dimension by incorporating speaker identity and turn position as additional dimensions in the discourse analysis framework. Beyond traditional sentence-level analysis, the model considers the multi-dimensional structure of multi-party dialogues including speaker roles, turn-taking patterns, and hierarchical discourse organization. This dimensional expansion enables more nuanced discourse relation detection and improves performance on complex multi-speaker interactions.