Utterance Politeness Classification With OOD Sampling

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Solution Overview

Problem

Conversational agents struggle to maintain politeness in their responses, leading to customer dissatisfaction, and lack effective out-of-distribution generalization, hindering smooth interactions.

Innovation Solution

Implementing a method for utterance classification using politeness scoring and linguistic term counting, combined with out-of-distribution data generalization through module optimization and de-biasing, to enhance the politeness and adaptability of conversational agents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conversational agents use standard response generation, then processing speed is maintained, but politeness and user satisfaction deteriorate

Engineering Contradiction:

ImprovepolitenessVSAvoidresponse generation efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system pre-generates multiple candidate responses with varying politeness levels and stores them in a response pool. During interaction, the appropriate pre-prepared response is selected based on the conversation context, avoiding the need to generate responses in real-time while ensuring polite and appropriate replies are provided.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system adjusts response parameters such as politeness level, formality, and tone based on the conversation context, user preferences, and situational factors. By dynamically changing these parameters, the system maintains high politeness standards without requiring complete response regeneration, thus preserving efficiency.

Inventive Principle:

Principle #35Parameter changes

2Productivity

If conversational agents are trained only on in-distribution data, then training efficiency is improved, but generalization to out-of-distribution scenarios deteriorates

Engineering Contradiction:

Improvetraining efficiencyVSAvoidout-of-distribution generalization

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system applies different data sampling strategies to different regions of the data distribution. In-distribution data is used for primary training to maintain efficiency, while out-of-distribution samples are specifically selected and applied to enhance generalization capabilities in edge cases and novel scenarios.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Instead of training exclusively on in-distribution data, the system incorporates a portion of out-of-distribution samples into the training process. This partial inclusion of diverse data types improves generalization without completely sacrificing training efficiency, as the majority of training still relies on well-structured in-distribution data.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260051332A1Annotating data for building conversational agents reinforcing politeness using multiple auxiliary models and out-of-distribution sampling

Publication Date: 2026.02.19 DELL PROD LP

AI summary

A method for utterance classification. The method includes: receiving an unclassified utterance; processing the unclassified utterance to produce a politeness score; analyzing the unclassified utterance to produce a key linguistic terms count; making a first determination that the politeness score exceeds a politeness score threshold; making a second determination, based on the first determination, that the key linguistic terms count exceeds a key linguistic terms count threshold; and classifying, based on the second determination, the unclassified utterance as a polite utterance.