Semantic Role Labeling for Domain-Independent Spoken Language Understanding
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Solution Overview
Problem
Existing spoken language understanding systems rely on domain-dependent approaches that require large amounts of labeled in-domain data, are expensive and time-consuming to prepare, and lack consistency across different applications, making them inefficient for intent classification and named entity extraction.
Innovation Solution
The use of semantic role labeling (SRL) to parse utterances, extract predicates and arguments, and estimate intents, allowing for a domain-independent approach that reduces the need for in-domain data and enhances consistency by mapping arguments to frame slots, independent of specific application domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-dependent approaches are used for intent classification and named entity extraction, then the systems can achieve accurate understanding within specific domains, but they require large amounts of labeled in-domain data which is expensive and time-consuming to prepare
Solution Approach 1:
The patent segments the language understanding task into semantic role labeling (identifying predicates and arguments) and intent classification (mapping arguments to frame slots). This segmentation allows the system to reuse semantic annotations across domains while only requiring domain-specific mapping rules, reducing the need for extensive domain-specific labeled data.
Solution Approach 2:
The patent introduces semantic role labeling as an intermediary layer between speech recognition and intent classification. This intermediary represents the utterance in a domain-independent semantic form (predicates and arguments) that can be mapped to different domain-specific frames, eliminating the need for extensive domain-specific training data.
2Measurement precision
If domain-dependent approaches are used for intent classification and named entity extraction, then the systems can achieve accurate understanding within specific domains, but they lack consistency across different applications
Solution Approach 1:
The patent makes the semantic role labeling component universal and domain-independent, capable of handling multiple domains through a single consistent framework. The same SRL pipeline (identifying predicates and arguments) serves all domains, while only the mapping from arguments to frame slots needs to be adapted to specific domains, ensuring consistency across applications.
3Reliability
If rule-based methods are used for intent classification and named entity extraction, then human expertise can be applied to design the system, but it still requires large amounts of labeled data and involves non-trivial design decisions for labeling guides
Solution Approach 1:
The patent extracts the domain-specific knowledge into separate mapping rules that connect semantic arguments to frame slots, separating it from the core semantic role labeling process. This extraction allows the SRL component to remain simple and domain-independent, while domain expertise is captured in reusable mapping rules, reducing overall system complexity.
Data Source
AI summary
An apparatus and a method are provided for using semantic role labeling for spoken language understanding. A received utterance semantically parsed by semantic role labeling. A predicate or at least one argument is extracted from the semantically parsed utterance. An intent is estimated based on the predicate or the at least one argument. In another aspect, a method is provided for training a spoken language dialog system that uses semantic role labeling. An expert is provided with a group of predicate/argument pairs. Ones of the predicate/argument pairs are selected as intents. Ones of the arguments are selected as named entities. Mappings from the arguments to frame slots are designed.