Multi-Party Dialogue Embeddings with Speaker-Aware DAGs
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Solution Overview
Problem
Computers and machine learning models struggle to comprehend multi-party dialogues due to difficulties in identifying conversational flows, assigning utterances to specific speakers, and handling out-of-order questions and responses, which creates noisy context.
Innovation Solution
A computer-implemented method that analyzes multi-party dialogues by leveraging speaker information, key utterances, and discourse to generate richer embeddings, allowing for complex modeling and querying of dialogues using natural language queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used to process multi-party dialogues, then the system is simple to implement, but the comprehension accuracy deteriorates due to difficulties in identifying conversational flows and assigning utterances to speakers
Solution Approach 1:
The patent segments the multi-party dialogue processing into distinct components: speaker identification module, utterance attribution module, and contextual relationship module. Each component handles a specific aspect of the dialogue comprehension, allowing the system to accurately track conversational flows and assign utterances to correct speakers without overwhelming complexity
Solution Approach 2:
The patent introduces an intermediary representation layer that mediates between raw dialogue inputs and final comprehension outputs. This intermediary layer structures the dialogue data into organized formats with speaker identifiers, utterance boundaries, and contextual relationships, enabling accurate comprehension while maintaining manageable system complexity
2Measurement precision
If the system processes all dialogue utterances in detail to improve comprehension, then the accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing dialogue data into structured formats with embedded speaker identifiers and utterance boundaries before main processing. This preliminary structuring enables faster subsequent processing while maintaining comprehensive analysis, as the data is already organized for efficient querying and analysis
Solution Approach 2:
The patent applies partial action by focusing processing resources on key utterances and critical conversational segments rather than uniformly processing all dialogue content. The system identifies and prioritizes important utterances based on contextual relevance, achieving accurate comprehension with reduced processing time by not over-processing less critical segments
3Measurement precision
If the system creates detailed representations of all dialogue elements to capture intricate relationships, then the comprehension quality improves, but the data complexity and noise increase
Solution Approach 1:
The patent applies local quality by creating detailed representations only for specific critical elements of the dialogue such as speaker identifiers, key utterances, and important contextual relationships. Less critical elements are represented more simply, allowing the system to capture intricate relationships where needed while avoiding unnecessary complexity and noise in other areas
4Measurement precision
If the system handles out-of-order questions and responses to improve completeness, then the comprehension coverage improves, but the contextual noise increases making comprehension harder
Solution Approach 1:
The patent performs preliminary reordering and structuring of out-of-order dialogue elements before main processing. The system identifies questions and responses regardless of their position in the dialogue stream, reorganizes them into logical sequences, and establishes proper contextual relationships in advance, enabling complete comprehension coverage while reducing contextual noise during actual analysis
Data Source
AI summary
The present disclosure describes complex modeling of dialogues that allows querying of the modeled dialogues. Embeddings may be generated for each multi-party dialogue of a plurality of multi-party dialogues. Embeddings may include speaker-aware embeddings, key-utterance embeddings, and/or discourse-aware embeddings. In addition to the embeddings, a directed acyclic graph (DAG) to show a relationship between the one or more utterances of the multi-party dialogue. The embeddings and the DAG may be stored in a datastore. In response to receiving a request to identify dialogues associated with a topic, the datastore may be queried to retrieve dialogues associated with the received topic. The dialogues may be provided to the requesting party, which may use the information retrieved from the datastore to respond to a requesting party. By leveraging speaker information, key utterances, and/or discourse, the present disclosure builds a richer knowledgebase of each multi-party dialogue and provides better responses to third-party inquiries.