Response Selection for Character-Like Dialogue Systems
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Solution Overview
Problem
Role play-based question answering systems lack evaluation of answer appropriateness, leading to potential output of inappropriate responses.
Innovation Solution
A response selecting apparatus with a record unit, document search unit, score calculation unit, and ranking unit that learns a translation model based on the degree to which an answer sounds like a character, ensuring more appropriate and character-like responses are generated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If role play-based question answering is used to collect question/answer pairs, then data collection efficiency is improved, but answer appropriateness deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by calculating scores for each question/answer pair based on translation likelihood from a learned translation model. This automated evaluation provides feedback on answer quality, allowing the system to select only appropriate answers while maintaining efficient data collection through role play-based methods.
Solution Approach 2:
The patent changes the parameter of answer selection from simple collection to scored evaluation. By introducing translation likelihood as a scoring parameter, the system transforms the data collection process into a quality-filtered selection process, ensuring answer appropriateness while maintaining efficiency.
2Reliability
If translation model learning is added to evaluate answer appropriateness, then answer quality is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-learning a translation model using collected question/answer pairs before the actual response selection process. This pre-processing step creates a ready-to-use evaluation mechanism that simplifies the ongoing operation, as the model can automatically score answers without requiring complex real-time analysis.
3Measurement precision
If scores are calculated for all question/answer pairs, then response selection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by calculating scores for question/answer pairs selectively rather than evaluating every possible pair exhaustively. The system uses the learned translation model to score pairs based on translation likelihood, focusing computational resources on evaluating the most relevant candidates, thus balancing accuracy with processing efficiency.
Data Source
AI summary
A model learning apparatus of the present invention has a question/answer-pair expansion unit and a translation-model learning unit. The question/answer-pair expansion unit generates expansion question/answer pairs by increasing the number of question/answer pairs associated with an index indicating that it sounds more like the character. The translation-model learning unit learns a translation model and a reverse translation model by using the expansion question/answer pairs. A response selecting apparatus of the present invention has a record unit, a document search unit, a score calculation unit, and a ranking unit. The record unit records question/answer pairs and the above described learned translation model. The score calculation unit obtains a translation likelihood which is a numerical value based on the probability of obtaining the answer from the input question and calculates a score of each of a search-result question/answer pair with respect to the input question.