Task type dialogue text enhancement system

Abstract

The invention discloses a task type dialogue text enhancement system, and relates to the technical field of text processing. The task type dialogue text enhancement system comprises a synonym replacement module, a synonym replacement module, an entity replacement module, a noise injection module and a text mixing module; the synonym replacement module is used for replacing corresponding words withsynonyms of each word in a text to generate first training data; the synonym replacement module is used for replacing the corresponding words with synonyms of the words in the text to generate secondtraining data; the entity replacement module is used for replacing corresponding entities with entities of the same type as the entities in the text to generate third training data; the noise injection module is used for emptying, exchanging, adding and deleting words of the text to generate fourth training data; and the text mixing module is used for mixing the first training data, the second training data, the third training data and the fourth training data to obtain fifth training data, so that the problem of insufficient training data is relieved, and the performance of the task type dialogue system is further optimized.

Description

technical field [0001] The invention relates to the technical field of text processing, in particular to a task-based dialogue text enhancement system. Background technique [0002] Task-based dialog systems are designed to help users complete practical and specific tasks, such as helping users search for music, book flights or hotels, etc. [0003] In terms of architecture, the task-based dialogue system mainly includes a natural language understanding module, a dialogue management module, and a natural language generation module. Among them, the natural language understanding module is mainly used for the intent recognition module and semantic filling. The dialogue management module is mainly used for dialogue state tracking and dialogue strategy optimization. The natural language generation module can be realized mainly through template configuration. Generally speaking, a task-based dialogue system contains multiple intents. When the user inputs a sentence, the inten...

AI Technical Summary

Problems solved by technology

In the process of building task-based dialogue systems, the scarcity of training data has always been a problem, which restricts the performance optimization process of task-based dialogue systems