Semantic Parse Graph Clustering for Spoken Language Understanding
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Solution Overview
Problem
Conventional spoken dialog systems struggle to accurately determine user intents in out-of-domain user utterances, as they rely on pre-determined domains and lack the ability to handle uncovered or new user intents, leading to unfulfilled user requests and limited accuracy in understanding naturally spoken language.
Innovation Solution
The utterance intent clustering technique involves semantically parsing user utterances into rooted semantic parse graphs, combining them into a corpus graph, and clustering them into intent-wise homogeneous groups, which are then used to train a spoken language understanding (SLU) classifier, enabling the identification of user intents across various domains, including uncovered ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-determined domains are used for classification, then classification speed is improved, but adaptability to new domains deteriorates
Solution Approach 1:
The system performs preliminary semantic parsing of user utterances into structured representations before classification. By pre-processing the utterances into standardized semantic forms, the system prepares the data in advance, enabling faster classification without sacrificing the ability to handle new domains through the flexible semantic parsing framework.
Solution Approach 2:
The system changes the parameter representation from fixed domain labels to flexible semantic parse graphs. This parameter transformation allows the classification to operate on structured semantic representations that can accommodate both known and new domains, resolving the contradiction between speed and adaptability.
2Manufacturing precision
If manual annotation is used for training, then training precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs self-service by automatically generating training data through semantic parsing of unannotated utterances. The semantic parse graphs serve as self-annotated training samples, eliminating the need for manual annotation while maintaining training quality. This allows the system to scale productivity without sacrificing training precision.
Solution Approach 2:
The system performs preliminary semantic parsing on large corpora of unannotated utterances to create structured training data in advance. This pre-processing transforms raw text into meaningful semantic representations that can be directly used for training, replacing the time-consuming manual annotation process while preserving training quality.
3Measurement precision
If semantic parsing is applied to all utterances, then measurement precision of user intent is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of intent determination into two stages: semantic parsing into structured graphs, followed by classification on these graphs. This segmentation simplifies the overall process by breaking down the complex semantic analysis into manageable components, improving both precision and tractability.
Solution Approach 2:
The semantic parse graph serves as an intermediary representation between raw utterances and final classification. This intermediate structured form simplifies the classification task by providing explicit semantic relationships, thereby improving measurement precision while managing system complexity through modular processing.
4Ease of operation
If out-of-domain utterances are handled with conventional methods, then ease of operation is maintained, but reliability deteriorates
Solution Approach 1:
The system employs a universal semantic parsing framework that handles both in-domain and out-of-domain utterances through the same process. The semantic parse graphs provide a domain-agnostic representation that maintains ease of operation while significantly improving reliability for out-of-domain cases by capturing the underlying semantic structure rather than relying on domain-specific patterns.
Data Source
AI summary
A system is provided that trains a spoken language understanding (SLU) classifier. A corpus of user utterances is received. For each of the user utterances in the corpus, the user utterance is semantically parsed, and the result of this semantic parsing is represented as a rooted semantic parse graph. The parse graphs representing all of the user utterances in the corpus are then combined into a single corpus graph that represents the semantic parses of the entire corpus. The user utterances in the corpus are then clustered into intent-wise homogeneous groups of user utterances, where this clustering includes finding subgraphs in the corpus graph that represent different groups of user utterances, and each of these different groups has a similar user intent. The intent-wise homogeneous groups of user utterances are then used to train the SLU classifier, and the trained SLU classifier is output.