Clarification Question Generation Using Layered Transformer and Pointer Generator
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Solution Overview
Problem
Existing methods for clarification question generation lack fuzzy semantic representation and inadequately handle Out-of-Vocabulary (OOV) issues, leading to inefficient processing of ambiguous expressions and misunderstandings in natural language processing.
Innovation Solution
A method incorporating a layered Transformer mechanism and a pointer generator mechanism into a coarse-to-fine CTF neural network model, combined with self-supervised learning auxiliary tasks like dialogue history prediction and entity name prediction, to enhance the generation of clarification questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods for clarification question generation are used, then the system can generate clarification questions, but the methods lack fuzzy semantic representation and do not deal with Out-of-Vocabulary (OOV) adequately
Solution Approach 1:
The patent divides the clarification question generation task into two separate modules: a template generating module that creates clarification question templates from fuzzy context, and an entity rendering module that fills entity information into templates. This segmentation allows each module to specialize in handling specific aspects (fuzzy semantics and OOV in template generation, entity information integration in rendering), thereby improving reliability without proportionally increasing overall system complexity.
Solution Approach 2:
The patent introduces a layered Transformer mechanism with multiple encoder layers and decoder layers, adding depth (another dimension) to the model architecture. This multi-layered structure enables the model to capture hierarchical semantic representations and effectively handle fuzzy semantics and OOV words through multi-level feature extraction and transformation.
2Reliability
If a pre-built CQG neural network model is used, then clarification question templates can be generated, but the model lacks adequate handling of Out-of-Vocabulary words
Solution Approach 1:
The patent introduces a pointer generator mechanism that acts as an intermediary between the encoder and decoder. This pointer generator enables the model to copy words directly from the input context (including OOV words) rather than being constrained to generate only from a predefined vocabulary, thereby improving OOV handling while maintaining adaptability to new vocabulary.
Solution Approach 2:
The patent implements a dynamic vocabulary selection mechanism where the model can adaptively choose between generating words from the predefined vocabulary (via softmax) or copying words from the input context (via pointer generator). This dynamic approach allows the model to flexibly handle both in-vocabulary and out-of-vocabulary words, improving both OOV handling and overall vocabulary coverage.
3Reliability
If fuzzy context is input into the template generating module, then clarification question templates can be obtained, but the system lacks fuzzy semantic representation
Solution Approach 1:
The patent employs a layered Transformer encoder with multiple layers that add depth to the semantic representation. Each layer transforms and refines the semantic features from the previous layer, creating hierarchical representations that capture fuzzy semantics more effectively. This multi-dimensional transformation approach improves semantic understanding while managing the complexity through structured layering.
Solution Approach 2:
The patent implements self-attention mechanisms within the Transformer encoder that provide feedback loops for semantic refinement. The attention mechanism allows the model to repeatedly refine its understanding of fuzzy semantic relationships by attending to relevant parts of the input context multiple times across different layers, improving fuzzy semantic representation through iterative feedback.
Data Source
AI summary
A method, a device and electronic device for clarification question generation are provided in one or more embodiments of this disclosure. The method includes: extracting entity information from a fuzzy context input by a user; inputting the fuzzy context into a template generating module of a pre-built CQG neural network model so as to obtain a clarification question template; inputting the entity information into an entity rendering module of the CQG neural network model so as to obtain at least one entity phrase; and generating a clarification question for a fuzzy question based on the clarification question template and the at least one entity phrase for presenting to the user.