Speech Processing Context Vectors for Anaphora Resolution
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Solution Overview
Problem
Existing speech processing systems face challenges in accurately resolving anaphora and maintaining context across multiple utterances, particularly when interacting with multiple speechlets, due to limitations in computational resources and reliance on explicit linguistic processing.
Innovation Solution
A distributed computing environment is employed, utilizing statistical analysis to resolve anaphora by encoding dialog context and entity data into vectors, scoring entities for relevance, and leveraging a network of local and remote devices to process user commands efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit linguistic processing is used to resolve anaphora, then anaphora resolution accuracy may be improved, but computational resource requirements increase
Solution Approach 1:
The system segments the anaphora resolution process into multiple components: dialog state tracking, entity scoring, and context vector encoding. This allows distributed computation across local and remote devices, reducing the computational burden on any single device while maintaining resolution accuracy through coordinated processing.
Solution Approach 2:
The patent introduces dialog context vectors as an intermediary representation that captures anaphoric relationships without requiring explicit linguistic processing. These vectors serve as a mediator between raw dialog text and anaphora resolution, enabling efficient computation while preserving accuracy through statistical patterns learned from training data.
2Measurement precision
If dialog context is maintained across multiple utterances, then context accuracy is improved, but system complexity increases
Solution Approach 1:
The dialog context vector serves multiple functions simultaneously: it tracks anaphoric references, maintains entity states, and captures dialog flow. This multi-functionality reduces system complexity by eliminating the need for separate mechanisms for each function, while still achieving high context accuracy through the unified vector representation.
Solution Approach 2:
The system transforms dialog context from a complex structured representation into a condensed vector form with fixed dimensions. This parameter change from detailed structured data to compact vectors maintains the essential contextual information needed for accuracy while significantly reducing the complexity of storage and processing.
3Productivity
If statistical analysis with vector encoding is used, then processing efficiency is improved, but anaphora resolution accuracy may deteriorate
Solution Approach 1:
The system performs preliminary encoding of dialog context and entity data into vectors before the actual anaphora resolution process. This preprocessing step organizes the data in a computationally efficient format that preserves semantic relationships, enabling fast processing without sacrificing accuracy because the vector encoding is designed to maintain anaphoric information.
Solution Approach 2:
The patent replaces explicit linguistic processing mechanisms with statistical vector-based processing. Instead of using rule-based linguistic analysis, the system uses learned vector representations that capture linguistic patterns statistically, achieving both efficiency through automated computation and accuracy through patterns learned from large training datasets.
Data Source
AI summary
A system for processing user utterances and/or text based queries that tracks entities and other context data of a current dialog between the system and the user and can fill slots for new intents of the dialog by performing statistical processing on previously mentioned entities with respect to current slots to be filled. The system may compare a previously mentioned entity to a current slot to be filled using vector representations, such as word embeddings, of the current utterance, dialog history, current intent, name of an entity under consideration, category of the current slot to be filled, distance between the current dialog turn and the dialog turn that mentioned the entity, and other considerations. The individual vectors may be weighted according to an attention operation and processed by a trained decoder to output a score indicating whether the entity in consideration is relevant to the particular slot. In this manner, slots may be filled using entities from previous dialog turns, thus performing statistical anaphora resolution and leading to improved system performance.