Neural Text Attribute Modification for Contextual Messaging
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Solution Overview
Problem
Existing messaging applications face challenges in suggesting messages that are contextually appropriate, as text aspects like time of day or sentiment may not align with the conversation's evolving context, leading to inappropriate or outdated suggestions.
Innovation Solution
A system that modifies specific attributes of pre-existing text messages by masking and replacing tokens using neural networks, allowing for dynamic adjustment of attributes such as sentiment, time, or rating while maintaining the core message intent, ensuring contextually relevant suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing text messages are used directly for suggestions, then the message pool is readily available and easy to implement, but the text aspects (time, sentiment, context) may not align with the current conversation context
Solution Approach 1:
The patent replaces manual text editing and rule-based context matching with a neural network language model that automatically generates and modifies messages. The model learns contextual patterns from training data and dynamically adapts message attributes (time, sentiment, tone) without requiring explicit programming of context rules.
Solution Approach 2:
The system modifies specific parameters of existing messages (time references, sentiment polarity, formality level) by conditioning the language model on desired attribute values. This allows flexible adjustment of message characteristics while maintaining the core message structure and intent.
2Adaptability or versatility
If a large pool of existing messages is maintained for suggestions, then message variety is improved, but selecting and adapting messages to match current context becomes more difficult
Solution Approach 1:
The system uses attention mechanisms that allow the language model to attend to relevant portions of the conversation history and previous messages, dynamically weighting their importance. This feedback loop enables the model to adjust message generation based on contextual cues from the ongoing dialogue.
Solution Approach 2:
The language model is pre-trained on large corpora of dialogue data to learn contextual patterns, language usage, and message structures beforehand. This preliminary training equips the model with the ability to rapidly adapt to new conversation contexts without requiring extensive real-time computation or manual curation.
3Reliability
If manual review and editing of suggested messages is performed, then message quality and appropriateness are improved, but time consumption and processing delay increase
Solution Approach 1:
The language model performs self-correction and quality assurance by generating messages that are inherently contextually appropriate due to its training on dialogue data. The model automatically adjusts for tone, timing, and relevance without requiring external human review, while still maintaining high message quality.
Data Source
AI summary
Text may be modified according to a specified attribute value, such as changing a 1-star review to a 5-star review. To modify the text, an input sequence of tokens may be obtained corresponding to the text. Mask scores may be computed for the tokens by processing the input sequence of tokens with masking neural network. One or more tokens may be selected using the mask scores, and a masked sequence of tokens may be generated by replacing the selected tokens with a mask token. The masked sequence of tokens may be processed by a language model neural network to select a replacement token for each of the mask tokens. Modified text may then be generated using the selected replacement tokens. The modified text may be used for any appropriate application, such as suggesting messages to users participating in a conversation.