Controllable Text Generation via Segmented Encoding and Hidden State Prediction
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Solution Overview
Problem
Existing controllable text generation methods face challenges in producing high-quality text that meets desired attributes due to the complexity of mapping relationships between input texts and controllable attributes, leading to suboptimal generation quality.
Innovation Solution
The method splits the learning goals into high-quality response and attribute satisfaction by encoding a first text, predicting a hidden state based on the coding sequence and controllable attribute, and then decoding to generate a second text that conforms to the attribute, improving the semantic relevance and quality of the generated text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing controllable text generation methods are used, then the text can be generated with desired attributes, but the mapping relationship complexity leads to suboptimal generation quality
Solution Approach 1:
The patent segments the text generation process into distinct components: an encoder that processes input text into coding sequences, a hidden state prediction module that processes controllable attributes, and a decoder that generates output text. This segmentation separates the complex mapping relationship into manageable modules, improving generation quality while reducing overall system complexity.
Solution Approach 2:
The patent introduces intermediate representations (coding sequences and hidden states) as mediators between the input text and controllable attributes. These intermediaries simplify the direct mapping relationship by breaking it down into sequential transformations, thereby reducing complexity while maintaining generation quality.
2Manufacturing precision
If the mapping process is simplified, then the generation quality improves, but the ability to meet specific controllable attributes may be compromised
Solution Approach 1:
The patent performs preliminary processing of controllable attributes by predicting hidden states before the actual text generation. This preliminary action ensures that attribute information is properly encoded and integrated into the generation process, maintaining attribute satisfaction while enabling simplified subsequent decoding operations.
Solution Approach 2:
The patent transforms the attribute satisfaction problem into a different dimension by mapping controllable attributes to hidden states in a latent space. This dimensional transformation allows the system to maintain reliable attribute control while using simplified decoding operations to generate high-quality text.
Data Source
AI summary
The disclosure provides a method for generating a text. The method includes: obtaining a coding sequence of a first text by coding the first text; obtaining a controllable attribute of a second text to be generated; predicting a hidden state of the second text based on the coding sequence of the first text and the controllable attribute of the second text; and obtaining a second text corresponding to the first text by decoding the coding sequence of the first text based on the hidden state of the second text.