Event-Based Semantic Search for Contextual Dialogue Retrieval
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Solution Overview
Problem
Current semantic clustering-based information retrieval systems are limited to searching individual utterances without considering their context, failing to leverage dialogue context for searching coherently-meaningful sequences of utterances or physical actions within longer transcripts.
Innovation Solution
The implementation of event-based semantic search and retrieval, where a set of annotated conversations is used to train a machine learning model to identify speech acts and organize sequences into events, allowing for the recognition and tagging of events in unannotated conversations, enabling context-based querying and retrieval of semantically-relevant conversations or fragments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic clustering-based search is used to search individual utterances, then search capability for individual words is improved, but the ability to search for coherently-meaningful sequences of utterances with contextual understanding deteriorates
Solution Approach 1:
The patent implements a hierarchical structure where utterances are nested within events, and events are nested within conversations. This nested organization allows the system to maintain both fine-grained utterance-level indexing and coarse-grained event-level contextual understanding, resolving the contradiction between precise individual search and contextual sequence search
Solution Approach 2:
The patent adds a new dimension of event-level abstraction above the traditional utterance level. By creating event representations that capture sequences of utterances with their contextual relationships, the system enables search in this new dimensional space, allowing users to search for coherent sequences while maintaining the original utterance-level search capability
2Adaptability or versatility
If event-based semantic search is implemented to enable contextual search of conversation sequences, then contextual understanding capability is improved, but system complexity increases
Solution Approach 1:
The system employs machine learning models that automatically learn event representations and contextual relationships from conversation data without requiring manual annotation of event boundaries or structures. The models self-organize the conversation data into events based on learned patterns, reducing the complexity burden on the system architecture
Solution Approach 2:
The patent introduces event representations as intermediary structures between raw utterances and search queries. These event intermediaries capture contextual relationships in a structured format that simplifies the search process, acting as a mediator that translates complex contextual understanding into searchable form without requiring the entire system to handle full contextual complexity at every level
3Measurement precision
If manual annotation of conversations is performed to train classifiers for event recognition, then accuracy of event identification is improved, but time and resource consumption increases
Solution Approach 1:
The system performs preliminary automated processing of conversation data using machine learning models to identify potential events and their structures before final classification. This preliminary action creates pre-processed event candidates that require minimal manual verification, significantly reducing the time and resources needed for manual annotation while maintaining high accuracy
Solution Approach 2:
The system implements feedback loops where manually annotated events are used to train and refine machine learning models, which then automatically process larger volumes of data. The feedback from manual annotation improves model accuracy over time, reducing the proportion of data requiring manual annotation while maintaining or improving overall event identification accuracy
Data Source
AI summary
A technique for semantic search and retrieval that is event-based, wherein is event is composed of a sequence of observations that are user speech or physical actions. Using a first set of conversations, a machine learning model is trained against groupings of utterances therein to generate a speech act classifier. Observation sequences therein are organized into groupings of events and configured for subsequent event recognition. A set of second (unannotated) conversations are then received. The set of second conversations is evaluated using the speech act classifier and information retrieved from the event recognition to generate event-level metadata that comprises, for each utterance or physical action within an event, one or more associated tags. In response to a query, a search is performed against the metadata. Because the metadata is derived from event recognition, the search is performed against events learned from the set of first conversations. One or more conversation fragments that, from an event-based perspective, are semantically-relevant to the query, are returned.