Communicative Discourse Tree for Explanation Request Detection
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Solution Overview
Problem
Current autonomous agents lack the ability to determine when a user's utterance includes a request for an explanation, which is crucial for transparency in decision-making processes, especially when machine learning models provide responses that are not transparent about their reasoning.
Innovation Solution
The use of communicative discourse trees in conjunction with machine learning to identify requests for explanations by representing text as a tree structure and applying a classification model trained to detect rhetorical relationships and thematic roles within the text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous agents use machine learning to generate responses, then the productivity and speed of answering user questions is improved, but the transparency and explainability of decision-making processes deteriorates
Solution Approach 1:
The system implements feedback by detecting explanation requests in user utterances and responding accordingly. When a user's utterance contains an explanation request (detected through discourse tree analysis), the autonomous agent provides explanatory responses that reveal the reasoning behind machine learning decisions, thus restoring transparency while maintaining productivity
Solution Approach 2:
The discourse tree analysis system acts as an intermediary between the user's utterance and the autonomous agent's response generation. It analyzes the rhetorical structure and thematic roles of the utterance to detect explanation requests, serving as a mediator that enables transparent communication without compromising the efficiency of machine learning-based responses
2Loss of information
If autonomous agents provide detailed explanations for machine learning decisions, then the transparency and user understanding is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the analysis of user utterances into distinct components: discourse tree construction, rhetorical relationship identification, and explanation request detection. This segmentation allows the complex task of explaining machine learning decisions to be broken down into manageable analytical steps, reducing overall system complexity while maintaining transparency
Solution Approach 2:
The discourse tree analysis applies different analytical approaches to different parts of the utterance based on local characteristics. By identifying specific rhetorical relationships and thematic roles in relevant portions of the text, the system provides targeted explanations where needed rather than uniformly complex processing throughout, thus managing system complexity effectively
3Measurement precision
If autonomous agents analyze user utterances for explanation requests using discourse trees, then the accuracy of detecting explanation requests is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing user utterances into discourse tree structures and pre-identifying rhetorical relationships and thematic roles. This preliminary analysis enables accurate detection of explanation requests while optimizing processing time, as the structural framework is established before the actual detection task
Data Source
AI summary
Systems, devices, and methods of the present invention relate to detecting a request for explanation in text. In an example, a method creates a discourse tree from a subset of text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The method forms a communicative discourse tree from the discourse tree by matching each fragment that has a verb to a verb signature. The method further identifies that the subset of text comprises a request for an explanation by applying a classification model trained to detect a request for an explanation to the communicative discourse tree.