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Self-adaptive theme story ending generation method and storage medium

An adaptive and thematic technology, applied in the field of task-oriented story ending generation, can solve problems such as limitations and difficulty in improving the performance of dissimilar target theme models, and achieve the effect of solving adaptive problems and accelerating adaptation

Pending Publication Date: 2021-12-07
XIAMEN UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the meta-learning framework is combined with the story ending generation system, the quality of the topic-sharing parameters learned in the training phase is closely related to, or even limited by, the distribution of source topic data used for training.
At the same time, in the test phase, the meta-learning framework can only be effectively adapted to similar topics, and it is still difficult to improve the performance of dissimilar target topic models.

Method used

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  • Self-adaptive theme story ending generation method and storage medium
  • Self-adaptive theme story ending generation method and storage medium
  • Self-adaptive theme story ending generation method and storage medium

Examples

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specific Embodiment

[0085] A computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for generating an adaptive theme story ending can be realized. The specific steps will not be repeated here, please See discussion above.

[0086] The story ending generation model based on the theme-sensitive meta-learning algorithm provided by the present invention, based on the parameter generator and topic adapter in the meta-learning framework, can train the story by using the high-resource information in the story ending generation task The end generative model enables it to efficiently adapt to target topics, especially low-resource topics, and thus be effectively boosted on multi-subject datasets. In addition, the model provided by the present invention has a clear structure and a clear idea, which can improve the adaptability of the unified story ending generation model to different themes. In addition, the topic-se...

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Abstract

The invention provides a self-adaptive theme story ending generation method and a storage medium. The method comprises the following steps: initializing a story ending generation model; randomly sampling data of a specific theme of a theme, and dividing the data into a training set and a test set; generating theme characteristic parameters corresponding to the specific theme of the theme through a parameter generator; combining the theme feature parameters with theme sharing parameters extracted from a story ending generation model through a theme adapter, and generating story ending generation model parameters corresponding to the specific theme of the theme; and generating a story end generation model parameter for each topic-specific topic. The unified model offset parameter related to the theme is generated and acts on the theme sharing parameter part in the unified model, so that the unified model can integrate theme specific guidance generated by the meta-learning framework while reserving theme sharing knowledge, the adaptation of the unified model to the target theme is accelerated, and finally, the self-adaptive story ending generation of the low-resource theme is realized.

Description

technical field [0001] The invention relates to the field of task-oriented story ending generation, in particular to an adaptive theme story ending generation method and a storage medium. Background technique [0002] Among task-oriented story generation tasks, story ending generation is a popular task. Its purpose is to give a story text and complete an ending for it. How to produce a coherent and logical ending to the preamble of the story is the key to research. For this task, the existing well-labeled data sets are very limited and expensive. Therefore, how to use the rich data of high-resource topic stories to enhance the performance of the system on low-resource topics and link stories of different topics is an important issue in academia and industry. research questions of great concern. [0003] In related art, previous research attempts to introduce external knowledge to guide the system to encode text and discover hidden information beneath the surface of the te...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06F40/166G06F40/20
CPCG06N3/08G06F40/166G06F40/20G06N3/048G06N3/045
Inventor 苏劲松康立言曾嘉莉
Owner XIAMEN UNIV
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